Generate realistic interpolations between turbulent flows with an adversarially-constrained autoencoder
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Updated
Jun 5, 2019 - Jupyter Notebook
Generate realistic interpolations between turbulent flows with an adversarially-constrained autoencoder
Basic deep learning models in PyTorch.
Deep learning framework for model reduction of dynamical systems
Simplified implementation of locally adaptive activation functions (LAAF) with slope recovery for deep and physics-informed neural networks (PINNs) in PyTorch.
My pytorch based implementation of the paper 'Limitations of Physics Informed Machine Learning for Nonlinear Two-Phase Transport in Porous Media'
Using Physics Informed Neural Networks to solve the Burger's Equation
Implementation in TF 2.0 of Maziar Raissi's Physics Informed Neural Networks (PINNs) repository.
Hydrodynamic image with the artificial lateral line using physics-informed informed neural networks and other proven methods in 2D dipole localization.
Short intro to scientific machine learning using physics informed neuronal networks. I used PyTorch as a framework.
The lid-driven cavity is a popular problem within the field of computational fluid dynamics (CFD) for validating computational methods. In this repository, we will walk through the process of generating a 2D flow simulation for the Lid Driven Cavity (LDC) flow using Nvidia Modulus framework.
Physics-based machine learning with dynamic Boltzmann distributions
Projektausarbeitung zum Wahlmodul - Sondergebiete der Simulation - WS21/22
Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed computing
Code accompanying my blog post: So, what is a physics-informed neural network?
Repository for the research project "Helmholtz--Hodge physics-informed neural networks".
Implementation of a Physics Informed Neural Network (PINN) written in Tensorflow v2, which is capable of solving Partial Differential Equations.
An interface for accelerated simulation of high-dimensional collisionless and electrostatic plasmas.
neural-network-based ROM testbed using an auto-encoder as an approximation manifold for the state, and an MLP for the reduced residual function
Physics-informed neural networks (PINNs)
A simple Physics Informed Neural Network (PINN) implementation in TensorFlow 2 for magnetic field mapping using finite amount of magnetic field measurements.
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