tum-pbs/differentiable-piso

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Created almost 3 years ago
Updated about 2 years ago

tum-pbs/differentiable-piso

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Code repository for "Learned Turbulence Modelling with Differentiable Fluid Solvers"

PhiFlow

A differentiable PDE solving framework for machine learning

pbdl-book

Welcome to the Physics-based Deep Learning Book (v0.2)

Jupyter Notebook

Solver-in-the-Loop

Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers

PhiML

Intuitive scientific computing with dimension types for Jax, PyTorch, TensorFlow & NumPy

autoreg-pde-diffusion

Benchmarking Autoregressive Conditional Diffusion Models for Turbulent Flow Simulation

Jupyter Notebook

DMCF

Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics (NeurIPS '22)

Diffusion-based-Flow-Prediction

Official implementation of the AIAA Journal paper "Uncertainty-aware Surrogate Models for Airfoil Flow Simulations with Denoising Diffusion Probabilistic Models"

Jupyter Notebook

dl-surrogates

LSIM

LSiM is a learned metric to compute distance values for 2D data from numerical simulations

coord-trans-encoding

This is the source code for our paper "Towards high-accuracy deep learning inference of compressible turbulent flows over aerofoils"

CG-Solver-in-the-Loop

Conjugate Gradient related code for "Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers"

ConFIG

Official implementation of Conflict-Free Inverse Gradients Method

SMDP

Solving Inverse Physics Problems with Score Matching

Jupyter Notebook

Global-Flow-Transport

Repository for our CVPR 2021 Global Flow Transport Paper

half-inverse-gradients

Source code for the ICLR'22 paper on "Half-Inverse Gradients"

VOLSIM

VolSiM, a CNN-based metric to compute the similarity of 3D data from numerical simulations

SIP

Scale-invariant Learning by Physics Inversion (NeurIPS 2022)

DiffPhys-CylinderWakeFlow

Jupyter Notebook

StableBPTT

The source code the for the ICLR'24 paper "Stabilizing Backpropagation Through Time to Learn Complex Physics"

unrolling

reconstructScalarFlows

ScalarFlow Reconstruction for Large-Scale Volumetric Data Sets of Real-world Scalar Transport Flows

SFBC

ICLR'24: Symmetric basis convolutions for learning lagrangian fluid mechanics

Jupyter Notebook

racecar

Data-driven Regularization via Racecar Training for Generalizing Neural Networks

two-way-coupled-control

Neural-Global-Transport

Repository for the ICLR '23 paper on Neural-Global-Transport

Hybrid-Solver-for-Reactive-Flows