diff --git a/torax/physics.py b/torax/physics.py
index aa58431b..46a4e31c 100644
--- a/torax/physics.py
+++ b/torax/physics.py
@@ -23,6 +23,7 @@
 import chex
 import jax
 from jax import numpy as jnp
+
 from torax import array_typing
 from torax import constants
 from torax import geometry
@@ -406,6 +407,23 @@ def _calculate_lambda_ei(
   """
   return 15.2 - 0.5 * jnp.log(ne / 1e20) + jnp.log(temp_el)
 
+def _calculate_lambda_ee(
+    temp_el: jax.Array,
+    ne: jax.Array,
+) -> jax.Array:
+  """Calculates Coulomb logarithm for electron-ion collisions.
+
+  See Wesson 3rd edition p727.
+
+  Args:
+    temp_el: Electron temperature in keV.
+    ne: Electron density in m^-3.
+
+  Returns:
+    Coulomb logarithm.
+  """
+  return 14.9 - 0.5 * jnp.log(ne / 1e20) + jnp.log(temp_el)
+
 
 def fast_ion_fractional_heating_formula(
     birth_energy: float | array_typing.ArrayFloat,
diff --git a/torax/transport_model/tglf_based_transport_model.py b/torax/transport_model/tglf_based_transport_model.py
new file mode 100644
index 00000000..27aa9b68
--- /dev/null
+++ b/torax/transport_model/tglf_based_transport_model.py
@@ -0,0 +1,206 @@
+# Copyright 2024 DeepMind Technologies Limited
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Base class and utils for TGLF-based models."""
+
+import chex
+from jax import numpy as jnp
+
+from torax import geometry
+from torax import physics
+from torax import state
+from torax.constants import CONSTANTS
+from torax.transport_model import quasilinear_transport_model
+from torax.transport_model import runtime_params as runtime_params_lib
+
+
+@chex.dataclass
+class RuntimeParams(quasilinear_transport_model.RuntimeParams):
+    """Shared parameters for TGLF-based models."""
+
+    def make_provider(
+        self, torax_mesh: geometry.Grid1D | None = None
+    ) -> "RuntimeParamsProvider":
+        return RuntimeParamsProvider(**self.get_provider_kwargs(torax_mesh))
+
+
+@chex.dataclass(frozen=True)
+class DynamicRuntimeParams(quasilinear_transport_model.DynamicRuntimeParams):
+    """Shared parameters for TGLF-based models."""
+
+    pass
+
+
+@chex.dataclass
+class RuntimeParamsProvider(runtime_params_lib.RuntimeParamsProvider):
+    """Provides a RuntimeParams to use during time t of the sim."""
+
+    runtime_params_config: RuntimeParams
+
+    def build_dynamic_params(self, t: chex.Numeric) -> DynamicRuntimeParams:
+        return DynamicRuntimeParams(**self.get_dynamic_params_kwargs(t))
+
+
+@chex.dataclass(frozen=True)
+class TGLFInputs(quasilinear_transport_model.QuasilinearInputs):
+    r"""Dimensionless inputs to the TGLF model.
+
+    See https://gafusion.github.io/doc/tglf/tglf_table.html for definitions.
+    """
+
+    # Ti/Te
+    Ti_over_Te: chex.Array
+    # dRmaj/dr
+    dRmaj: chex.Array
+    # q
+    q: chex.Array
+    # r/q dq/dr
+    s_hat: chex.Array
+    # nu_ee (see note in prepare_tglf_inputs)
+    nu_ee: chex.Array
+    # Elongation, kappa
+    kappa: chex.Array
+    # Shear in elongation, r/kappa dkappa/dr
+    kappa_shear: chex.Array
+    # Triangularity, delta
+    delta: chex.Array
+    # Shear in triangularity, r ddelta/dr
+    delta_shear: chex.Array
+    # Electron pressure defined w.r.t B_unit
+    beta_e: chex.Array
+    # Effective charge
+    Zeff: chex.Array
+
+
+class TGLFBasedTransportModel(quasilinear_transport_model.QuasilinearTransportModel):
+    """Base class for TGLF-based transport models."""
+
+    def _prepare_tglf_inputs(
+        Zeff_face: chex.Array,
+        nref: chex.Numeric,
+        q_correction_factor: chex.Numeric,
+        transport: DynamicRuntimeParams,
+        geo: geometry.Geometry,
+        core_profiles: state.CoreProfiles,
+    ) -> TGLFInputs:
+        # Note: TGLF uses geo.rmid = (Rmax - Rmin)/2 as the radial coordinate
+        # This means all gradients are calculated w.r.t. rmid
+
+        ## Shorthand for commonly used variables
+        Te = core_profiles.temp_el
+        Ti = core_profiles.temp_ion
+        ne = core_profiles.ne
+
+        ## Reference velocity and length, used for normalisation
+        # https://gafusion.github.io/doc/cgyro/outputs.html#output-normalization
+        vref = (Te.face_value() / (core_profiles.Ai * CONSTANTS.mp)) ** 0.5
+        lref = geo.Rmin[-1]  # Minor radius at LCFS
+
+        ## Temperature gradients, At = -1/T * dT/drho
+        # Note: RLTS = a_minor / T * dT/dr = 1 / T * dT/drho
+        # https://gafusion.github.io/doc/tglf/tglf_table.html#id2
+        Ti_over_Te = Ti.face_value() / Te.face_value()
+        Ate = -1 / Te.face_value() * Te.face_grad(geo.rmid)
+        Ati = -1 / Ti.face_value() * Ti.face_grad(geo.rmid)
+
+        # Density gradient, Ane = -1/ne * dne/drho
+        # Note: RLNS = a_minor / ne * dne/dr = 1 / ne * dne/drho
+        # https://gafusion.github.io/doc/tglf/tglf_table.html#id2
+        # Note: nref cancels, as 1/(ne*nref) * (ne_grad * nref) = 1/ne * ne_grad
+        Ane = -1 / ne.face_value() * core_profiles.ne.face_grad(geo.rmid)
+
+        ## Electron-electron collision frequency = nu_ee / (vref/lref)
+        # https://gafusion.github.io/doc/tglf/tglf_list.html#xnue
+        # https://gafusion.github.io/doc/cgyro/cgyro_list.html#cgyro-nu-ee
+        # Note: In the TGLF docs, XNUE is mislabelled as electron-ion collision frequency.
+        # It is actually the electron-electron collision frequency, and is defined as in CGYRO
+        # See https://pyrokinetics.readthedocs.io/en/latest/user_guide/collisions.html#tglf
+        Lambda_ee = physics._calculate_lambda_ee(Te, ne)
+        normalised_nu_ee = (4 * jnp.pi * ne * CONSTANTS.qe**4 * Lambda_ee) / (
+            CONSTANTS.me**0.5 * (2 * Te) ** 1.5
+        )
+        nu_ee = normalised_nu_ee / (vref / lref)
+
+        ## Safety factor, q
+        # https://gafusion.github.io/doc/tglf/tglf_list.html#q-sa
+        # Need to recalculate since in the nonlinear solver psi has intermediate
+        # states in the iterative solve
+        q, _ = physics.calc_q_from_psi(
+            geo=geo,
+            psi=core_profiles.psi,
+            q_correction_factor=q_correction_factor,
+        )
+
+        ## Safety factor shear, s_hat = r/q dq/dr
+        # https://gafusion.github.io/doc/tglf/tglf_list.html#tglf-shat-sa
+        # calc_s_from_psi_rmid gives rq dq/dr
+        s_hat = physics.calc_s_from_psi_rmid(geo, core_profiles.psi) / q**2
+
+        ## Electron beta
+        # https://gafusion.github.io/doc/tglf/tglf_list.html#tglf-betae
+        p_e = ne * (Te * 1e3)  # ne in m^-3, Te in eV
+        # B_unit = q/r dpsi/dr
+        B_unit = (
+            q / geo.rmid * jnp.gradient(core_profiles.psi, geo.rmid)
+        )
+        beta_e = 8 * jnp.pi * p_e / B_unit**2
+
+        ## Major radius shear = dRmaj/dr
+        # https://gafusion.github.io/doc/tglf/tglf_list.html#tglf-drmajdx-loc
+        dRmaj = jnp.gradient(geo.Rmaj, geo.rmid)
+
+        ## Elongation shear = r/kappa dkappa/dr
+        # https://gafusion.github.io/doc/tglf/tglf_list.html#tglf-s-kappa-loc
+        kappa = geo.elongation_face
+        kappa_shear = geo.rmid_face / kappa * jnp.gradient(kappa, geo.rmid_face)
+
+        ## Triangularity shear = r ddelta/dr
+        # https://gafusion.github.io/doc/tglf/tglf_list.html#tglf-s-delta-loc
+        delta = geo.delta_face
+        delta_shear = geo.rmid_face * jnp.gradient(delta, geo.rmid_face)
+
+        ## Gyrobohm diffusivity
+        # https://gafusion.github.io/doc/tglf/tglf_table.html#id7
+        # https://gafusion.github.io/doc/cgyro/outputs.html#output-normalization
+        # Note: TGLF uses the same normalisation as CGYRO, ie
+        # chiGB = ne * vref * T_e * (rho_s_unit / lref)**2
+        # where rho_s_unit = vref / (e*B_unit/ m_D / c)
+        # TODO: Check if this code implementation is correct/equivalent to the above
+        chiGB = (
+            (core_profiles.Ai * CONSTANTS.mp) ** 0.5
+            / (CONSTANTS.qe * geo.B0) ** 2
+            * (Ti.face_value() * CONSTANTS.keV2J) ** 1.5
+            / lref
+        )
+
+        return TGLFInputs(
+            # From QuasilinearInputs
+            chiGB=chiGB,
+            Rmin=geo.Rmin,
+            Rmaj=geo.Rmaj,
+            Ati=Ati,
+            Ate=Ate,
+            Ane=Ane,
+            # From TGLFInputs
+            Ti_over_Te=Ti_over_Te,
+            dRmaj=dRmaj,
+            q=q,
+            s_hat=s_hat,
+            nu_ee=nu_ee,
+            kappa=kappa,
+            kappa_shear=kappa_shear,
+            delta=delta,
+            delta_shear=delta_shear,
+            beta_e=beta_e,
+            Zeff=Zeff_face,
+        )
diff --git a/torax/transport_model/tglfnn.py b/torax/transport_model/tglfnn.py
new file mode 100644
index 00000000..f8a12116
--- /dev/null
+++ b/torax/transport_model/tglfnn.py
@@ -0,0 +1,105 @@
+import chex
+import jax.numpy as jnp
+from flax import linen as nn
+
+
+class TGLFNN(nn.Module):
+    """A simple MLP with dropout layers, ReLU activation, and outputting a mean and variance."""
+
+    hidden_dimension: int
+    n_hidden_layers: int
+    dropout: float
+    input_means: chex.Array
+    input_stds: chex.Array
+    output_mean: float
+    output_std: float
+
+    @nn.compact
+    def __call__(
+        self,
+        x,
+        deterministic: bool = False,
+        standardise_inputs: bool = True,
+        standardise_outputs: bool = False,
+    ):
+        if standardise_inputs:
+            # Transform to 0 mean and unit variance
+            x = (x - self.input_means) / self.input_stds
+
+        x = nn.Dense(self.hidden_dimension)(x)
+        x = nn.Dropout(rate=self.dropout, deterministic=deterministic)(x)
+        x = nn.relu(x)
+        for _ in range(self.n_hidden_layers):
+            x = nn.Dense(self.hidden_dimension)(x)
+            x = nn.Dropout(rate=self.dropout, deterministic=deterministic)(x)
+            x = nn.relu(x)
+        mean_and_var = nn.Dense(2)(x)
+        mean = mean_and_var[..., 0]
+        var = mean_and_var[..., 1]
+        var = nn.softplus(var)
+
+        if not standardise_outputs:
+            # Transform back from 0 mean and unit variance
+            mean = mean * self.output_std + self.output_mean
+            var = var * self.output_std**2
+
+        return jnp.stack([mean, var], axis=-1)
+
+
+class EnsembleTGLFNN(nn.Module):
+    """An ensemble of TGLFNN models."""
+
+    input_means: chex.Array
+    input_stds: chex.Array
+    output_mean: chex.Array
+    output_std: chex.Array
+    n_models: int = 5
+    hidden_dimension: int = 512
+    n_hidden_layers: int = 4
+    dropout: float = 0.05
+
+    def setup(
+        self,
+    ):
+        self.models = [
+            TGLFNN(
+                hidden_dimension=self.hidden_dimension,
+                n_hidden_layers=self.n_hidden_layers,
+                dropout=self.dropout,
+                input_means=self.input_means,
+                input_stds=self.input_stds,
+                output_mean=self.output_mean,
+                output_std=self.output_std,
+            )
+            for i in range(self.n_models)
+        ]
+
+    def __call__(self, x, *args, **kwargs):
+        # Shape is batch size x 2 x n_models
+        outputs = jnp.stack(
+            [model(x, *args, **kwargs) for model in self.models], axis=-1
+        )
+        # Shape is batch_size
+        mean = jnp.mean(outputs[:, 0, :], axis=-1)
+        aleatoric_uncertainty = jnp.mean(outputs[:, 1, :], axis=-1)
+        epistemic_uncertainty = jnp.var(outputs[:, 0, :], axis=-1)
+        return jnp.stack([mean, aleatoric_uncertainty + epistemic_uncertainty], axis=-1)
+
+    def get_params_from_pytorch_state_dict(self, pytorch_state_dict: dict):
+        params = {}
+        for i in range(self.n_models):
+            model_dict = {}
+            for j in range(self.n_hidden_layers + 2):  # +2 for input and output layers
+                # j*3 to skip dropout and activation
+                layer_dict = {
+                    "kernel": jnp.array(
+                        pytorch_state_dict[f"models.{i}.model.{j*3}.weight"]
+                    ).T,
+                    "bias": jnp.array(
+                        pytorch_state_dict[f"models.{i}.model.{j*3}.bias"]
+                    ).T,
+                }
+                model_dict[f"Dense_{j}"] = layer_dict
+            params[f"models_{i}"] = model_dict
+
+        return params