mariusmerkle
Interested in physics-informed neural networks (PINNs).
Technical University of MunichMunich
mariusmerkle's Stars
quantprep/quantnewgrad2022
borchero/natural-posterior-network
Official Implementation of "Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions" (ICLR, 2022)
Harvard-IACS/c_cpp_primer
IACS C/C++ primer
dbgannon/NNets-and-Diffeqns
jupyter notebooks for the neural nets and differential equation paper
beauCoker/bayesian_neural_networks
PyDMD/PyDMD
Python Dynamic Mode Decomposition
dynamicslab/pysindy
A package for the sparse identification of nonlinear dynamical systems from data
HIPS/autograd
Efficiently computes derivatives of NumPy code.
mariusmerkle/cs107_marius_merkle
Marius Merkle's GitHub repository for AC 207 - Systems Development for Computational Science
Harvard-IACS/2021-CS109A
chr1shr/am205_examples
Harvard Applied Math 205: Code Examples
NeuroDiffGym/neurodiffeq
A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.
ehsanhaghighat/sciann-applications
A place to share problems solved with SciANN
ehsanhaghighat/sciann
Deep learning for Engineers - Physics Informed Deep Learning
pierremtb/PINNs-TF2.0
TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).
SciML/SciMLTutorials.jl
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
SciML/NeuralPDE.jl
Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
SciML/DifferentialEquations.jl
Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
lululxvi/deepxde
A library for scientific machine learning and physics-informed learning
maziarraissi/PINNs
Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations