diff --git a/src/scgenerator/const.py b/src/scgenerator/const.py
index b8843a2..bfd5c25 100644
--- a/src/scgenerator/const.py
+++ b/src/scgenerator/const.py
@@ -25,3 +25,80 @@ SPEC1_FN_N = "spectrum_{}_{}.npy"
 Z_FN = "z.npy"
 PARAM_FN = "params.toml"
 PARAM_SEPARATOR = " "
+
+VALID_VARIABLE = {
+    "dispersion_file",
+    "prev_data_dir",
+    "field_file",
+    "loss_file",
+    "A_eff_file",
+    "beta2_coefficients",
+    "gamma",
+    "pitch",
+    "pitch_ratio",
+    "effective_mode_diameter",
+    "core_radius",
+    "model",
+    "capillary_num",
+    "capillary_radius",
+    "capillary_thickness",
+    "capillary_spacing",
+    "capillary_resonance_strengths",
+    "capillary_resonance_max_order",
+    "capillary_nested",
+    "he_mode",
+    "fit_parameters",
+    "input_transmission",
+    "n2",
+    "pressure",
+    "temperature",
+    "gas_name",
+    "plasma_density",
+    "peak_power",
+    "mean_power",
+    "peak_power",
+    "energy",
+    "quantum_noise",
+    "shape",
+    "wavelength",
+    "intensity_noise",
+    "width",
+    "t0",
+    "soliton_num",
+    "behaviors",
+    "raman_type",
+    "tolerated_error",
+    "step_size",
+    "interpolation_degree",
+    "ideal_gas",
+    "length",
+    "num",
+}
+
+MANDATORY_PARAMETERS = [
+    "name",
+    "w_c",
+    "w",
+    "w0",
+    "w_power_fact",
+    "alpha",
+    "spec_0",
+    "field_0",
+    "mean_power",
+    "input_transmission",
+    "z_targets",
+    "length",
+    "beta2_coefficients",
+    "gamma_arr",
+    "behaviors",
+    "raman_type",
+    "hr_w",
+    "adapt_step_size",
+    "tolerated_error",
+    "dynamic_dispersion",
+    "recovery_last_stored",
+    "output_path",
+    "repeat",
+    "linear_operator",
+    "nonlinear_op",
+]
diff --git a/src/scgenerator/evaluator.py b/src/scgenerator/evaluator.py
new file mode 100644
index 0000000..a374929
--- /dev/null
+++ b/src/scgenerator/evaluator.py
@@ -0,0 +1,383 @@
+from typing import Optional, Callable, Union, Any
+from dataclasses import dataclass
+from .physics import fiber, pulse, materials, units
+from .utils import _mock_function, get_arg_names, get_logger, func_rewrite
+from .errors import *
+from collections import defaultdict
+from .const import MANDATORY_PARAMETERS
+import numpy as np
+import itertools
+from . import math, utils, operators
+
+
+class Rule:
+    def __init__(
+        self,
+        target: Union[str, list[Optional[str]]],
+        func: Callable,
+        args: list[str] = None,
+        priorities: Union[int, list[int]] = None,
+        conditions: dict[str, str] = None,
+    ):
+        targets = list(target) if isinstance(target, (list, tuple)) else [target]
+        self.func = func
+        if priorities is None:
+            priorities = [1] * len(targets)
+        elif isinstance(priorities, (int, float, np.integer, np.floating)):
+            priorities = [priorities]
+        self.targets = dict(zip(targets, priorities))
+        if args is None:
+            args = get_arg_names(func)
+        self.args = args
+        self.mock_func = _mock_function(len(self.args), len(self.targets))
+        self.conditions = conditions or {}
+
+    def __repr__(self) -> str:
+        return f"Rule(targets={self.targets!r}, func={self.func!r}, args={self.args!r})"
+
+    def __str__(self) -> str:
+        return f"[{', '.join(self.args)}] -- {self.func.__module__}.{self.func.__name__} --> [{', '.join(self.targets)}]"
+
+    @classmethod
+    def deduce(
+        cls,
+        target: Union[str, list[Optional[str]]],
+        func: Callable,
+        kwarg_names: list[str],
+        n_var: int,
+        args_const: list[str] = None,
+        priorities: Union[int, list[int]] = None,
+    ) -> list["Rule"]:
+        """given a function that doesn't need all its keyword arguemtn specified, will
+        return a list of Rule obj, one for each combination of n_var specified kwargs
+
+        Parameters
+        ----------
+        target : str | list[str | None]
+            name of the variable(s) that func returns
+        func : Callable
+            function to work with
+        kwarg_names : list[str]
+            list of all kwargs of the function to be used
+        n_var : int
+            how many shoulf be used per rule
+        arg_const : list[str], optional
+            override the name of the positional arguments
+
+        Returns
+        -------
+        list[Rule]
+            list of all possible rules
+
+        Example
+        -------
+        >> def lol(a, b=None, c=None):
+            pass
+        >> print(Rule.deduce(["d"], lol, ["b", "c"], 1))
+        [
+            Rule(targets={'d': 1}, func=<function lol_0 at 0x7f9bce31d0d0>, args=['a', 'b']),
+            Rule(targets={'d': 1}, func=<function lol_0 at 0x7f9bce31d160>, args=['a', 'c'])
+        ]
+        """
+        rules: list[cls] = []
+        for var_possibility in itertools.combinations(kwarg_names, n_var):
+
+            new_func = func_rewrite(func, list(var_possibility), args_const)
+
+            rules.append(cls(target, new_func, priorities=priorities))
+        return rules
+
+
+@dataclass
+class EvalStat:
+    priority: float = np.inf
+
+
+class Evaluator:
+    defaults: dict[str, Any] = {}
+
+    @classmethod
+    def default(cls) -> "Evaluator":
+        evaluator = cls()
+        evaluator.append(*default_rules)
+        return evaluator
+
+    @classmethod
+    def evaluate_default(cls, params: dict[str, Any], check_only=False) -> dict[str, Any]:
+        evaluator = cls.default()
+        evaluator.set(**params)
+        for target in MANDATORY_PARAMETERS:
+            evaluator.compute(target, check_only=check_only)
+        return evaluator.params
+
+    @classmethod
+    def register_default_param(cls, key, value):
+        cls.defaults[key] = value
+
+    def __init__(self):
+        self.rules: dict[str, list[Rule]] = defaultdict(list)
+        self.params = {}
+        self.__curent_lookup: list[str] = []
+        self.__failed_rules: dict[str, list[Rule]] = defaultdict(list)
+        self.eval_stats: dict[str, EvalStat] = defaultdict(EvalStat)
+        self.logger = get_logger(__name__)
+
+    def append(self, *rule: Rule):
+        for r in rule:
+            for t in r.targets:
+                if t is not None:
+                    self.rules[t].append(r)
+                    self.rules[t].sort(key=lambda el: el.targets[t], reverse=True)
+
+    def set(self, **params: Any):
+        self.params.update(params)
+        for k in params:
+            self.eval_stats[k].priority = np.inf
+
+    def reset(self):
+        self.params = {}
+        self.eval_stats = defaultdict(EvalStat)
+
+    def compute(self, target: str, check_only=False) -> Any:
+        """computes a target
+
+        Parameters
+        ----------
+        target : str
+            name of the target
+
+        Returns
+        -------
+        Any
+            return type of the target function
+
+        Raises
+        ------
+        EvaluatorError
+            a cyclic dependence exists
+        KeyError
+            there is no saved rule for the target
+        """
+        value = self.params.get(target)
+        if value is None:
+            prefix = "\t" * len(self.__curent_lookup)
+            # Avoid cycles
+            if target in self.__curent_lookup:
+                raise EvaluatorError(
+                    "cyclic dependency detected : "
+                    f"{target!r} seems to depend on itself, "
+                    f"please provide a value for at least one variable in {self.__curent_lookup!r}. "
+                    + self.attempted_rules_str(target)
+                )
+            else:
+                self.__curent_lookup.append(target)
+
+            if len(self.rules[target]) == 0:
+                error = EvaluatorError(f"no rule for {target}")
+            else:
+                error = None
+
+            # try every rule until one succeeds
+            for ii, rule in enumerate(filter(self.validate_condition, self.rules[target])):
+                self.logger.debug(
+                    prefix + f"attempt {ii+1} to compute {target}, this time using {rule!r}"
+                )
+                try:
+                    args = [self.compute(k, check_only=check_only) for k in rule.args]
+                    if check_only:
+                        returned_values = rule.mock_func(*args)
+                    else:
+                        returned_values = rule.func(*args)
+                    if len(rule.targets) == 1:
+                        returned_values = [returned_values]
+                    for ((param_name, param_priority), returned_value) in zip(
+                        rule.targets.items(), returned_values
+                    ):
+                        if (
+                            param_name == target
+                            or param_name not in self.params
+                            or self.eval_stats[param_name].priority < param_priority
+                        ):
+                            if check_only:
+                                success_str = f"able to compute {param_name} "
+                            else:
+                                v_str = format(returned_value).replace("\n", "")
+                                success_str = f"computed {param_name}={v_str} "
+                            self.logger.info(
+                                prefix
+                                + success_str
+                                + f"using {rule.func.__name__} from {rule.func.__module__}"
+                            )
+                            self.set_value(param_name, returned_value, param_priority)
+                        if param_name == target:
+                            value = returned_value
+                    break
+                except (EvaluatorError, KeyError, NoDefaultError) as e:
+                    error = e
+                    self.logger.debug(
+                        prefix + f"error using {rule.func.__name__} : {str(error).strip()}"
+                    )
+                    self.__failed_rules[target].append(rule)
+                    continue
+            else:
+                default = self.defaults.get(target)
+                if default is None:
+                    error = error or NoDefaultError(
+                        prefix
+                        + f"No default provided for {target}. Current lookup cycle : {self.__curent_lookup!r}. "
+                        + self.attempted_rules_str(target)
+                    )
+                else:
+                    value = default
+                    self.logger.info(prefix + f"using default value of {value} for {target}")
+                    self.set_value(target, value, 0)
+
+            assert target == self.__curent_lookup.pop()
+            self.__failed_rules[target] = []
+
+            if value is None and error is not None:
+                raise error
+
+        return value
+
+    def __getitem__(self, key: str) -> Any:
+        return self.params[key]
+
+    def set_value(self, key: str, value: Any, priority: int):
+        self.params[key] = value
+        self.eval_stats[key].priority = priority
+
+    def validate_condition(self, rule: Rule) -> bool:
+        return all(self.compute(k) == v for k, v in rule.conditions.items())
+
+    def attempted_rules_str(self, target: str) -> str:
+        rules = ", ".join(str(r) for r in self.__failed_rules[target])
+        if len(rules) == 0:
+            return ""
+        return "attempted rules : " + rules
+
+
+default_rules: list[Rule] = [
+    # Grid
+    *Rule.deduce(
+        ["z_targets", "t", "time_window", "t_num", "dt", "w_c", "w0", "w", "l"],
+        math.build_sim_grid,
+        ["time_window", "t_num", "dt"],
+        2,
+    ),
+    Rule("adapt_step_size", lambda step_size: step_size == 0),
+    Rule("dynamic_dispersion", lambda pressure: isinstance(pressure, (list, tuple, np.ndarray))),
+    # Pulse
+    Rule("spec_0", np.fft.fft, ["field_0"]),
+    Rule("field_0", np.fft.ifft, ["spec_0"]),
+    Rule("spec_0", utils.load_previous_spectrum, ["recovery_data_dir"], priorities=4),
+    Rule("spec_0", utils.load_previous_spectrum, priorities=3),
+    *Rule.deduce(
+        ["pre_field_0", "peak_power", "energy", "width"],
+        pulse.load_and_adjust_field_file,
+        ["energy", "peak_power"],
+        1,
+        priorities=[2, 1, 1, 1],
+    ),
+    Rule("pre_field_0", pulse.initial_field, priorities=1),
+    Rule(
+        "field_0",
+        pulse.finalize_pulse,
+        [
+            "pre_field_0",
+            "quantum_noise",
+            "w_c",
+            "w0",
+            "time_window",
+            "dt",
+            "additional_noise_factor",
+            "input_transmission",
+        ],
+    ),
+    Rule("peak_power", pulse.E0_to_P0, ["energy", "t0", "shape"]),
+    Rule("peak_power", pulse.soliton_num_to_peak_power),
+    Rule("mean_power", pulse.energy_to_mean_power),
+    Rule("energy", pulse.P0_to_E0, ["peak_power", "t0", "shape"]),
+    Rule("energy", pulse.mean_power_to_energy, priorities=2),
+    Rule("t0", pulse.width_to_t0),
+    Rule("t0", pulse.soliton_num_to_t0),
+    Rule("width", pulse.t0_to_width),
+    Rule("soliton_num", pulse.soliton_num),
+    Rule("L_D", pulse.L_D),
+    Rule("L_NL", pulse.L_NL),
+    Rule("L_sol", pulse.L_sol),
+    # Fiber Dispersion
+    Rule("wl_for_disp", fiber.lambda_for_dispersion),
+    Rule("w_for_disp", units.m, ["wl_for_disp"]),
+    Rule(
+        "beta2_coefficients",
+        fiber.dispersion_coefficients,
+        ["wl_for_disp", "beta2_arr", "w0", "interpolation_range", "interpolation_degree"],
+    ),
+    Rule("beta2_arr", fiber.beta2),
+    Rule("beta2_arr", fiber.dispersion_from_coefficients),
+    Rule("beta2", lambda beta2_coefficients: beta2_coefficients[0]),
+    Rule(
+        ["wl_for_disp", "beta2_arr", "interpolation_range"],
+        fiber.load_custom_dispersion,
+        priorities=[2, 2, 2],
+    ),
+    Rule("hr_w", fiber.delayed_raman_w),
+    Rule("n_gas_2", materials.n_gas_2),
+    Rule("n_eff", fiber.n_eff_hasan, conditions=dict(model="hasan")),
+    Rule("n_eff", fiber.n_eff_marcatili, conditions=dict(model="marcatili")),
+    Rule("n_eff", fiber.n_eff_marcatili_adjusted, conditions=dict(model="marcatili_adjusted")),
+    Rule(
+        "n_eff",
+        fiber.n_eff_pcf,
+        ["wl_for_disp", "pitch", "pitch_ratio"],
+        conditions=dict(model="pcf"),
+    ),
+    Rule("capillary_spacing", fiber.capillary_spacing_hasan),
+    Rule("capillary_resonance_strengths", fiber.capillary_resonance_strengths),
+    Rule("capillary_resonance_strengths", lambda: [], priorities=-1),
+    # Fiber nonlinearity
+    Rule("A_eff", fiber.A_eff_from_V),
+    Rule("A_eff", fiber.A_eff_from_diam),
+    Rule("A_eff", fiber.A_eff_hasan, conditions=dict(model="hasan")),
+    Rule("A_eff", fiber.A_eff_from_gamma, priorities=-1),
+    Rule("A_eff", fiber.A_eff_marcatili, priorities=-2),
+    Rule("A_eff_arr", fiber.A_eff_from_V, ["core_radius", "V_eff_arr"]),
+    Rule("A_eff_arr", fiber.load_custom_A_eff),
+    Rule("A_eff_arr", fiber.constant_A_eff_arr, priorities=-1),
+    Rule(
+        "V_eff",
+        fiber.V_parameter_koshiba,
+        ["wavelength", "pitch", "pitch_ratio"],
+        conditions=dict(model="pcf"),
+    ),
+    Rule("V_eff", fiber.V_eff_step_index, ["wavelength", "core_radius", "numerical_aperture"]),
+    Rule("V_eff_arr", fiber.V_parameter_koshiba, conditions=dict(model="pcf")),
+    Rule(
+        "V_eff_arr",
+        fiber.V_eff_step_index,
+        ["l", "core_radius", "numerical_aperture", "interpolation_range"],
+    ),
+    Rule("gamma", lambda gamma_arr: gamma_arr[0]),
+    Rule("gamma_arr", fiber.gamma_parameter, ["n2", "w0", "A_eff_arr"]),
+    Rule("n2", materials.gas_n2),
+    Rule("n2", lambda: 2.2e-20, priorities=-1),
+    # Operators
+    Rule("gamma_op", operators.ConstantGamma),
+    Rule("gamma_op", operators.NoGamma, priorities=-1),
+    Rule("ss_op", operators.SelfSteepening),
+    Rule("ss_op", operators.NoSelfSteepening, priorities=-1),
+    Rule("spm_op", operators.SPM),
+    Rule("spm_op", operators.NoSPM, priorities=-1),
+    Rule("raman_op", operators.Raman),
+    Rule("raman_op", operators.NoRaman, priorities=-1),
+    Rule("nonlinear_operator", operators.EnvelopeNonLinearOperator),
+    Rule("loss_op", operators.CustomConstantLoss, priorities=3),
+    Rule("loss_op", operators.CapillaryLoss, priorities=2),
+    Rule("loss_op", operators.ConstantLoss, priorities=1),
+    Rule("loss_op", operators.NoLoss, priorities=-1),
+    Rule("disp_op", operators.ConstantPolyDispersion),
+    Rule("linear_operator", operators.LinearOperator),
+    # gas
+    Rule("n_gas_2", materials.n_gas_2),
+]
diff --git a/src/scgenerator/physics/properties.py b/src/scgenerator/operators.py
similarity index 88%
rename from src/scgenerator/physics/properties.py
rename to src/scgenerator/operators.py
index 4d82dc2..b7396c8 100644
--- a/src/scgenerator/physics/properties.py
+++ b/src/scgenerator/operators.py
@@ -10,8 +10,8 @@ from dataclasses import dataclass, field
 import numpy as np
 from scipy.interpolate import interp1d
 
-from . import fiber
-from .. import math
+from .physics import fiber
+from . import math
 
 
 class SpectrumDescriptor:
@@ -45,6 +45,20 @@ class CurrentState:
         return self.z / self.length
 
 
+class Operator(ABC):
+    def __repr__(self) -> str:
+        return (
+            self.__class__.__name__
+            + "("
+            + ", ".join(k + "=" + repr(v) for k, v in self.__dict__.items())
+            + ")"
+        )
+
+    @abstractmethod
+    def __call__(self, state: CurrentState) -> np.ndarray:
+        pass
+
+
 class NoOp:
     def __init__(self, w: np.ndarray):
         self.zero_arr = np.zeros_like(w)
@@ -55,7 +69,7 @@ class NoOp:
 ##################################################
 
 
-class AbstractDispersion(ABC):
+class AbstractDispersion(Operator):
     @abstractmethod
     def __call__(self, state: CurrentState) -> np.ndarray:
         """returns the dispersion in the frequency domain
@@ -74,7 +88,7 @@ class AbstractDispersion(ABC):
 
 class ConstantPolyDispersion(AbstractDispersion):
     """
-    dispersion approximated by fitting a polynom on the dispersion and
+    dispersion approximated by fitting a polynome on the dispersion and
     evaluating on the envelope
     """
 
@@ -87,8 +101,8 @@ class ConstantPolyDispersion(AbstractDispersion):
         beta2_arr: np.ndarray,
         w0: float,
         w_c: np.ndarray,
-        interpolation_range: tuple[float, float] = None,
-        interpolation_degree: int = 8,
+        interpolation_range: tuple[float, float],
+        interpolation_degree: int,
     ):
         self.coefs = fiber.dispersion_coefficients(
             wl_for_disp, beta2_arr, w0, interpolation_range, interpolation_degree
@@ -108,9 +122,9 @@ class ConstantPolyDispersion(AbstractDispersion):
 
 
 class LinearOperator:
-    def __init__(self, disp: AbstractDispersion, loss: AbstractLoss):
-        self.disp = disp
-        self.loss = loss
+    def __init__(self, disp_op: AbstractDispersion, loss_op: AbstractLoss):
+        self.disp = disp_op
+        self.loss = loss_op
 
     def __call__(self, state: CurrentState) -> np.ndarray:
         """returns the linear operator to be multiplied by the spectrum in the frequency domain
@@ -135,7 +149,7 @@ class LinearOperator:
 # Raman
 
 
-class AbstractRaman(ABC):
+class AbstractRaman(Operator):
     f_r: float = 0.0
 
     @abstractmethod
@@ -171,7 +185,7 @@ class Raman(AbstractRaman):
 # SPM
 
 
-class AbstractSPM(ABC):
+class AbstractSPM(Operator):
     fraction: float = 1.0
 
     @abstractmethod
@@ -206,7 +220,7 @@ class SPM(AbstractSPM):
 # Selt Steepening
 
 
-class AbstractSelfSteepening(ABC):
+class AbstractSelfSteepening(Operator):
     @abstractmethod
     def __call__(self, state: CurrentState) -> np.ndarray:
         """returns the self-steepening component
@@ -239,7 +253,7 @@ class SelfSteepening(AbstractSelfSteepening):
 # Gamma operator
 
 
-class AbstractGamma(ABC):
+class AbstractGamma(Operator):
     @abstractmethod
     def __call__(self, state: CurrentState) -> np.ndarray:
         """returns the gamma component
@@ -275,7 +289,7 @@ class ConstantGamma(AbstractSelfSteepening):
 # Nonlinear combination
 
 
-class AbstractNonLinearOperator(ABC):
+class NonLinearOperator(Operator):
     @abstractmethod
     def __call__(self, state: CurrentState) -> np.ndarray:
         """returns the nonlinear operator applied on the spectrum in the frequency domain
@@ -292,7 +306,7 @@ class AbstractNonLinearOperator(ABC):
         """
 
 
-class EnvelopeNonLinearOperator(AbstractNonLinearOperator):
+class EnvelopeNonLinearOperator(NonLinearOperator):
     def __init__(
         self,
         gamma_op: AbstractGamma,
@@ -319,7 +333,7 @@ class EnvelopeNonLinearOperator(AbstractNonLinearOperator):
 ##################################################
 
 
-class AbstractLoss(ABC):
+class AbstractLoss(Operator):
     @abstractmethod
     def __call__(self, state: CurrentState) -> np.ndarray:
         """returns the loss in the frequency domain
@@ -342,10 +356,15 @@ class ConstantLoss(AbstractLoss):
     def __init__(self, alpha: float, w: np.ndarray):
         self.alpha_arr = alpha * np.ones_like(w)
 
-    def __call__(self, state: CurrentState) -> np.ndarray:
+    def __call__(self, state: CurrentState = None) -> np.ndarray:
         return self.alpha_arr
 
 
+class NoLoss(ConstantLoss):
+    def __init__(self, w: np.ndarray):
+        super().__init__(0, w)
+
+
 class CapillaryLoss(ConstantLoss):
     def __init__(
         self,
diff --git a/src/scgenerator/parameter.py b/src/scgenerator/parameter.py
index c6729c6..804d366 100644
--- a/src/scgenerator/parameter.py
+++ b/src/scgenerator/parameter.py
@@ -2,10 +2,8 @@ from __future__ import annotations
 
 import datetime as datetime_module
 import enum
-import inspect
 import itertools
 import os
-import re
 import time
 from collections import defaultdict
 from copy import copy
@@ -18,94 +16,18 @@ import numpy as np
 from numpy.lib import isin
 
 from . import env, math, utils
-from .const import PARAM_FN, __version__
-from .errors import EvaluatorError, NoDefaultError
+from .const import PARAM_FN, __version__, VALID_VARIABLE, MANDATORY_PARAMETERS
 from .logger import get_logger
-from .physics import fiber, materials, pulse, units
-from .utils import _mock_function, fiber_folder, func_rewrite, get_arg_names, update_path_name
+from .utils import fiber_folder, update_path_name
 from .variationer import VariationDescriptor, Variationer
+from .evaluator import Evaluator
+from .operators import NonLinearOperator, LinearOperator
 
 T = TypeVar("T")
 
 # Validator
 
 
-VALID_VARIABLE = {
-    "dispersion_file",
-    "prev_data_dir",
-    "field_file",
-    "loss_file",
-    "A_eff_file",
-    "beta2_coefficients",
-    "gamma",
-    "pitch",
-    "pitch_ratio",
-    "effective_mode_diameter",
-    "core_radius",
-    "model",
-    "capillary_num",
-    "capillary_radius",
-    "capillary_thickness",
-    "capillary_spacing",
-    "capillary_resonance_strengths",
-    "capillary_resonance_max_order",
-    "capillary_nested",
-    "he_mode",
-    "fit_parameters",
-    "input_transmission",
-    "n2",
-    "pressure",
-    "temperature",
-    "gas_name",
-    "plasma_density",
-    "peak_power",
-    "mean_power",
-    "peak_power",
-    "energy",
-    "quantum_noise",
-    "shape",
-    "wavelength",
-    "intensity_noise",
-    "width",
-    "t0",
-    "soliton_num",
-    "behaviors",
-    "raman_type",
-    "tolerated_error",
-    "step_size",
-    "interpolation_degree",
-    "ideal_gas",
-    "length",
-    "num",
-}
-
-MANDATORY_PARAMETERS = [
-    "name",
-    "w_c",
-    "w",
-    "w0",
-    "w_power_fact",
-    "alpha",
-    "spec_0",
-    "field_0",
-    "mean_power",
-    "input_transmission",
-    "z_targets",
-    "length",
-    "beta2_coefficients",
-    "gamma_arr",
-    "behaviors",
-    "raman_type",
-    "hr_w",
-    "adapt_step_size",
-    "tolerated_error",
-    "dynamic_dispersion",
-    "recovery_last_stored",
-    "output_path",
-    "repeat",
-]
-
-
 @lru_cache
 def type_checker(*types):
     def _type_checker_wrapper(validator, n=None):
@@ -286,6 +208,8 @@ class Parameter:
 
     def __set_name__(self, owner, name):
         self.name = name
+        if self.default is not None:
+            Evaluator.register_default_param(self.name, self.default)
 
     def __get__(self, instance, owner):
         if instance is None:
@@ -405,9 +329,7 @@ class Parameters(_AbstractParameters):
         validator_list(literal("spm", "raman", "ss")), converter=tuple, default=("spm", "ss")
     )
     parallel: bool = Parameter(boolean, default=True)
-    raman_type: str = Parameter(
-        literal("measured", "agrawal", "stolen"), converter=str.lower, default="agrawal"
-    )
+    raman_type: str = Parameter(literal("measured", "agrawal", "stolen"), converter=str.lower)
     ideal_gas: bool = Parameter(boolean, default=False)
     repeat: int = Parameter(positive(int), default=1)
     t_num: int = Parameter(positive(int))
@@ -423,6 +345,8 @@ class Parameters(_AbstractParameters):
     worker_num: int = Parameter(positive(int))
 
     # computed
+    linear_operator: LinearOperator = Parameter(type_checker(LinearOperator))
+    nonlinear_operator: NonLinearOperator = Parameter(type_checker(NonLinearOperator))
     field_0: np.ndarray = Parameter(type_checker(np.ndarray))
     spec_0: np.ndarray = Parameter(type_checker(np.ndarray))
     beta2: float = Parameter(type_checker(int, float))
@@ -538,253 +462,6 @@ class Parameters(_AbstractParameters):
         return None
 
 
-class Rule:
-    def __init__(
-        self,
-        target: Union[str, list[Optional[str]]],
-        func: Callable,
-        args: list[str] = None,
-        priorities: Union[int, list[int]] = None,
-        conditions: dict[str, str] = None,
-    ):
-        targets = list(target) if isinstance(target, (list, tuple)) else [target]
-        self.func = func
-        if priorities is None:
-            priorities = [1] * len(targets)
-        elif isinstance(priorities, (int, float, np.integer, np.floating)):
-            priorities = [priorities]
-        self.targets = dict(zip(targets, priorities))
-        if args is None:
-            args = get_arg_names(func)
-        self.args = args
-        self.mock_func = _mock_function(len(self.args), len(self.targets))
-        self.conditions = conditions or {}
-
-    def __repr__(self) -> str:
-        return f"Rule(targets={self.targets!r}, func={self.func!r}, args={self.args!r})"
-
-    def __str__(self) -> str:
-        return f"[{', '.join(self.args)}] -- {self.func.__module__}.{self.func.__name__} --> [{', '.join(self.targets)}]"
-
-    @classmethod
-    def deduce(
-        cls,
-        target: Union[str, list[Optional[str]]],
-        func: Callable,
-        kwarg_names: list[str],
-        n_var: int,
-        args_const: list[str] = None,
-        priorities: Union[int, list[int]] = None,
-    ) -> list["Rule"]:
-        """given a function that doesn't need all its keyword arguemtn specified, will
-        return a list of Rule obj, one for each combination of n_var specified kwargs
-
-        Parameters
-        ----------
-        target : str | list[str | None]
-            name of the variable(s) that func returns
-        func : Callable
-            function to work with
-        kwarg_names : list[str]
-            list of all kwargs of the function to be used
-        n_var : int
-            how many shoulf be used per rule
-        arg_const : list[str], optional
-            override the name of the positional arguments
-
-        Returns
-        -------
-        list[Rule]
-            list of all possible rules
-
-        Example
-        -------
-        >> def lol(a, b=None, c=None):
-            pass
-        >> print(Rule.deduce(["d"], lol, ["b", "c"], 1))
-        [
-            Rule(targets={'d': 1}, func=<function lol_0 at 0x7f9bce31d0d0>, args=['a', 'b']),
-            Rule(targets={'d': 1}, func=<function lol_0 at 0x7f9bce31d160>, args=['a', 'c'])
-        ]
-        """
-        rules: list[cls] = []
-        for var_possibility in itertools.combinations(kwarg_names, n_var):
-
-            new_func = func_rewrite(func, list(var_possibility), args_const)
-
-            rules.append(cls(target, new_func, priorities=priorities))
-        return rules
-
-
-@dataclass
-class EvalStat:
-    priority: float = np.inf
-
-
-class Evaluator:
-    @classmethod
-    def default(cls) -> "Evaluator":
-        evaluator = cls()
-        evaluator.append(*default_rules)
-        return evaluator
-
-    @classmethod
-    def evaluate_default(cls, params: dict[str, Any], check_only=False) -> dict[str, Any]:
-        evaluator = cls.default()
-        evaluator.set(**params)
-        for target in MANDATORY_PARAMETERS:
-            evaluator.compute(target, check_only=check_only)
-        return evaluator.params
-
-    def __init__(self):
-        self.rules: dict[str, list[Rule]] = defaultdict(list)
-        self.params = {}
-        self.__curent_lookup: list[str] = []
-        self.__failed_rules: dict[str, list[Rule]] = defaultdict(list)
-        self.eval_stats: dict[str, EvalStat] = defaultdict(EvalStat)
-        self.logger = get_logger(__name__)
-
-    def append(self, *rule: Rule):
-        for r in rule:
-            for t in r.targets:
-                if t is not None:
-                    self.rules[t].append(r)
-                    self.rules[t].sort(key=lambda el: el.targets[t], reverse=True)
-
-    def set(self, **params: Any):
-        self.params.update(params)
-        for k in params:
-            self.eval_stats[k].priority = np.inf
-
-    def reset(self):
-        self.params = {}
-        self.eval_stats = defaultdict(EvalStat)
-
-    def get_default(self, key: str) -> Any:
-        try:
-            return getattr(Parameters, key).default
-        except AttributeError:
-            return None
-
-    def compute(self, target: str, check_only=False) -> Any:
-        """computes a target
-
-        Parameters
-        ----------
-        target : str
-            name of the target
-
-        Returns
-        -------
-        Any
-            return type of the target function
-
-        Raises
-        ------
-        EvaluatorError
-            a cyclic dependence exists
-        KeyError
-            there is no saved rule for the target
-        """
-        value = self.params.get(target)
-        if value is None:
-            prefix = "\t" * len(self.__curent_lookup)
-            # Avoid cycles
-            if target in self.__curent_lookup:
-                raise EvaluatorError(
-                    "cyclic dependency detected : "
-                    f"{target!r} seems to depend on itself, "
-                    f"please provide a value for at least one variable in {self.__curent_lookup!r}. "
-                    + self.attempted_rules_str(target)
-                )
-            else:
-                self.__curent_lookup.append(target)
-
-            if len(self.rules[target]) == 0:
-                error = EvaluatorError(f"no rule for {target}")
-            else:
-                error = None
-
-            # try every rule until one succeeds
-            for ii, rule in enumerate(filter(self.validate_condition, self.rules[target])):
-                self.logger.debug(
-                    prefix + f"attempt {ii+1} to compute {target}, this time using {rule!r}"
-                )
-                try:
-                    args = [self.compute(k, check_only=check_only) for k in rule.args]
-                    if check_only:
-                        returned_values = rule.mock_func(*args)
-                    else:
-                        returned_values = rule.func(*args)
-                    if len(rule.targets) == 1:
-                        returned_values = [returned_values]
-                    for ((param_name, param_priority), returned_value) in zip(
-                        rule.targets.items(), returned_values
-                    ):
-                        if (
-                            param_name == target
-                            or param_name not in self.params
-                            or self.eval_stats[param_name].priority < param_priority
-                        ):
-                            if check_only:
-                                success_str = f"able to compute {param_name} "
-                            else:
-                                v_str = format(returned_value).replace("\n", "")
-                                success_str = f"computed {param_name}={v_str} "
-                            self.logger.info(
-                                prefix
-                                + success_str
-                                + f"using {rule.func.__name__} from {rule.func.__module__}"
-                            )
-                            self.set_value(param_name, returned_value, param_priority)
-                        if param_name == target:
-                            value = returned_value
-                    break
-                except (EvaluatorError, KeyError, NoDefaultError) as e:
-                    error = e
-                    self.logger.debug(
-                        prefix + f"error using {rule.func.__name__} : {str(error).strip()}"
-                    )
-                    self.__failed_rules[target].append(rule)
-                    continue
-            else:
-                default = self.get_default(target)
-                if default is None:
-                    error = error or NoDefaultError(
-                        prefix
-                        + f"No default provided for {target}. Current lookup cycle : {self.__curent_lookup!r}. "
-                        + self.attempted_rules_str(target)
-                    )
-                else:
-                    value = default
-                    self.logger.info(prefix + f"using default value of {value} for {target}")
-                    self.set_value(target, value, 0)
-
-            assert target == self.__curent_lookup.pop()
-            self.__failed_rules[target] = []
-
-            if value is None and error is not None:
-                raise error
-
-        return value
-
-    def __getitem__(self, key: str) -> Any:
-        return self.params[key]
-
-    def set_value(self, key: str, value: Any, priority: int):
-        self.params[key] = value
-        self.eval_stats[key].priority = priority
-
-    def validate_condition(self, rule: Rule) -> bool:
-        return all(self.compute(k) == v for k, v in rule.conditions.items())
-
-    def attempted_rules_str(self, target: str) -> str:
-        rules = ", ".join(str(r) for r in self.__failed_rules[target])
-        if len(rules) == 0:
-            return ""
-        return "attempted rules : " + rules
-
-
 class Configuration:
     """
     Primary role is to load the final config file of the simulation and deduce every
@@ -1041,120 +718,6 @@ class Configuration:
             return param
 
 
-default_rules: list[Rule] = [
-    # Grid
-    *Rule.deduce(
-        ["z_targets", "t", "time_window", "t_num", "dt", "w_c", "w0", "w", "l"],
-        math.build_sim_grid,
-        ["time_window", "t_num", "dt"],
-        2,
-    ),
-    Rule("adapt_step_size", lambda step_size: step_size == 0),
-    Rule("dynamic_dispersion", lambda pressure: isinstance(pressure, (list, tuple, np.ndarray))),
-    # Pulse
-    Rule("spec_0", np.fft.fft, ["field_0"]),
-    Rule("field_0", np.fft.ifft, ["spec_0"]),
-    Rule("spec_0", utils.load_previous_spectrum, ["recovery_data_dir"], priorities=4),
-    Rule("spec_0", utils.load_previous_spectrum, priorities=3),
-    *Rule.deduce(
-        ["pre_field_0", "peak_power", "energy", "width"],
-        pulse.load_and_adjust_field_file,
-        ["energy", "peak_power"],
-        1,
-        priorities=[2, 1, 1, 1],
-    ),
-    Rule("pre_field_0", pulse.initial_field, priorities=1),
-    Rule(
-        "field_0",
-        pulse.finalize_pulse,
-        [
-            "pre_field_0",
-            "quantum_noise",
-            "w_c",
-            "w0",
-            "time_window",
-            "dt",
-            "additional_noise_factor",
-            "input_transmission",
-        ],
-    ),
-    Rule("peak_power", pulse.E0_to_P0, ["energy", "t0", "shape"]),
-    Rule("peak_power", pulse.soliton_num_to_peak_power),
-    Rule("mean_power", pulse.energy_to_mean_power),
-    Rule("energy", pulse.P0_to_E0, ["peak_power", "t0", "shape"]),
-    Rule("energy", pulse.mean_power_to_energy, priorities=2),
-    Rule("t0", pulse.width_to_t0),
-    Rule("t0", pulse.soliton_num_to_t0),
-    Rule("width", pulse.t0_to_width),
-    Rule("soliton_num", pulse.soliton_num),
-    Rule("L_D", pulse.L_D),
-    Rule("L_NL", pulse.L_NL),
-    Rule("L_sol", pulse.L_sol),
-    # Fiber Dispersion
-    Rule("wl_for_disp", fiber.lambda_for_dispersion),
-    Rule("w_for_disp", units.m, ["wl_for_disp"]),
-    Rule(
-        "beta2_coefficients",
-        fiber.dispersion_coefficients,
-        ["wl_for_disp", "beta2_arr", "w0", "interpolation_range", "interpolation_degree"],
-    ),
-    Rule("beta2_arr", fiber.beta2),
-    Rule("beta2_arr", fiber.dispersion_from_coefficients),
-    Rule("beta2", lambda beta2_coefficients: beta2_coefficients[0]),
-    Rule(
-        ["wl_for_disp", "beta2_arr", "interpolation_range"],
-        fiber.load_custom_dispersion,
-        priorities=[2, 2, 2],
-    ),
-    Rule("hr_w", fiber.delayed_raman_w),
-    Rule("n_gas_2", materials.n_gas_2),
-    Rule("n_eff", fiber.n_eff_hasan, conditions=dict(model="hasan")),
-    Rule("n_eff", fiber.n_eff_marcatili, conditions=dict(model="marcatili")),
-    Rule("n_eff", fiber.n_eff_marcatili_adjusted, conditions=dict(model="marcatili_adjusted")),
-    Rule(
-        "n_eff",
-        fiber.n_eff_pcf,
-        ["wl_for_disp", "pitch", "pitch_ratio"],
-        conditions=dict(model="pcf"),
-    ),
-    Rule("capillary_spacing", fiber.capillary_spacing_hasan),
-    Rule("capillary_resonance_strengths", fiber.capillary_resonance_strengths),
-    Rule("capillary_resonance_strengths", lambda: [], priorities=-1),
-    # Fiber nonlinearity
-    Rule("A_eff", fiber.A_eff_from_V),
-    Rule("A_eff", fiber.A_eff_from_diam),
-    Rule("A_eff", fiber.A_eff_hasan, conditions=dict(model="hasan")),
-    Rule("A_eff", fiber.A_eff_from_gamma, priorities=-1),
-    Rule("A_eff", fiber.A_eff_marcatili, priorities=-2),
-    Rule("A_eff_arr", fiber.A_eff_from_V, ["core_radius", "V_eff_arr"]),
-    Rule("A_eff_arr", fiber.load_custom_A_eff),
-    Rule("A_eff_arr", fiber.constant_A_eff_arr, priorities=-1),
-    Rule(
-        "V_eff",
-        fiber.V_parameter_koshiba,
-        ["wavelength", "pitch", "pitch_ratio"],
-        conditions=dict(model="pcf"),
-    ),
-    Rule("V_eff", fiber.V_eff_step_index, ["wavelength", "core_radius", "numerical_aperture"]),
-    Rule("V_eff_arr", fiber.V_parameter_koshiba, conditions=dict(model="pcf")),
-    Rule(
-        "V_eff_arr",
-        fiber.V_eff_step_index,
-        ["l", "core_radius", "numerical_aperture", "interpolation_range"],
-    ),
-    Rule("gamma", lambda gamma_arr: gamma_arr[0]),
-    Rule("gamma_arr", fiber.gamma_parameter, ["n2", "w0", "A_eff_arr"]),
-    Rule("n2", materials.gas_n2),
-    Rule("n2", lambda: 2.2e-20, priorities=-1),
-    # Fiber loss
-    Rule("alpha_arr", fiber.compute_capillary_loss),
-    Rule("alpha_arr", fiber.load_custom_loss),
-    Rule("alpha_arr", lambda alpha, t: np.ones_like(t) * alpha, priorities=-1),
-    # gas
-    Rule("n_gas_2", materials.n_gas_2),
-]
-
-
 if __name__ == "__main__":
 
     numero = type_checker(int)
diff --git a/src/scgenerator/physics/fiber.py b/src/scgenerator/physics/fiber.py
index 9b33cae..5b63a16 100644
--- a/src/scgenerator/physics/fiber.py
+++ b/src/scgenerator/physics/fiber.py
@@ -843,19 +843,6 @@ def load_custom_loss(l: np.ndarray, loss_file: str) -> np.ndarray:
     return interp1d(wl, loss, fill_value=0, bounds_error=False)(l)
 
 
-def compute_capillary_loss(
-    l: np.ndarray,
-    core_radius: float,
-    interpolation_range: tuple[float, float],
-    he_mode: tuple[int, int],
-) -> np.ndarray:
-    mask = (l < interpolation_range[1]) & (l > 0)
-    alpha = capillary_loss(l[mask], he_mode, core_radius)
-    out = np.zeros_like(l)
-    out[mask] = alpha
-    return out
-
-
 @np_cache
 def dispersion_coefficients(
     wl_for_disp: np.ndarray,
diff --git a/src/scgenerator/physics/simulate.py b/src/scgenerator/physics/simulate.py
index 0f6094f..dfe7189 100644
--- a/src/scgenerator/physics/simulate.py
+++ b/src/scgenerator/physics/simulate.py
@@ -2,8 +2,8 @@ import multiprocessing
 import multiprocessing.connection
 import os
 import random
-from datetime import datetime
 from dataclasses import dataclass
+from datetime import datetime
 from pathlib import Path
 from typing import Any, Generator, Type, Union
 
@@ -13,9 +13,9 @@ from .. import utils
 from ..logger import get_logger
 from ..parameter import Configuration, Parameters
 from ..pbar import PBars, ProgressBarActor, progress_worker
+from ..operators import CurrentState
 from . import pulse
 from .fiber import create_non_linear_op, fast_dispersion_op
-from .properties import CurrentState
 
 try:
     import ray
diff --git a/src/scgenerator/utils.py b/src/scgenerator/utils.py
index 1e95d85..6d5acd2 100644
--- a/src/scgenerator/utils.py
+++ b/src/scgenerator/utils.py
@@ -4,12 +4,13 @@ scgenerator module but some function may be used in any python program
 
 """
 from __future__ import annotations
-from dataclasses import dataclass
+
 import inspect
 import itertools
 import os
 import re
 from collections import defaultdict
+from dataclasses import dataclass
 from functools import cache
 from pathlib import Path
 from string import printable as str_printable
@@ -373,11 +374,19 @@ def to_62(i: int) -> str:
 
 
 def get_arg_names(func: Callable) -> list[str]:
-    # spec = inspect.getfullargspec(func)
-    # args = spec.args
-    # if spec.defaults is not None and len(spec.defaults) > 0:
-    #     args = args[: -len(spec.defaults)]
-    # return args
+    """returns the positional argument names of func.
+
+    Parameters
+    ----------
+    func : Callable
+        if a function, returns the names of the positional arguments
+
+
+    Returns
+    -------
+    list[str]
+        [description]
+    """
     return [k for k, v in inspect.signature(func).parameters.items() if v.default is inspect._empty]