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Physics-informed machine learning
Despite great progress in simulating multiphysics problems using the numerical
discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate …
discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate …
Reconstructing computational system dynamics from neural data with recurrent neural networks
Computational models in neuroscience usually take the form of systems of differential
equations. The behaviour of such systems is the subject of dynamical systems theory …
equations. The behaviour of such systems is the subject of dynamical systems theory …
Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems
Despite the great promise of the physics-informed neural networks (PINNs) in solving
forward and inverse problems, several technical challenges are present as roadblocks for …
forward and inverse problems, several technical challenges are present as roadblocks for …
PhyCRNet: Physics-informed convolutional-recurrent network for solving spatiotemporal PDEs
Partial differential equations (PDEs) play a fundamental role in modeling and simulating
problems across a wide range of disciplines. Recent advances in deep learning have shown …
problems across a wide range of disciplines. Recent advances in deep learning have shown …
β-Variational autoencoders and transformers for reduced-order modelling of fluid flows
Variational autoencoder architectures have the potential to develop reduced-order models
for chaotic fluid flows. We propose a method for learning compact and near-orthogonal …
for chaotic fluid flows. We propose a method for learning compact and near-orthogonal …
Scalable transformer for pde surrogate modeling
Transformer has shown state-of-the-art performance on various applications and has
recently emerged as a promising tool for surrogate modeling of partial differential equations …
recently emerged as a promising tool for surrogate modeling of partial differential equations …
[HTML][HTML] Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels
High-resolution (HR) information of fluid flows, although preferable, is usually less
accessible due to limited computational or experimental resources. In many cases, fluid data …
accessible due to limited computational or experimental resources. In many cases, fluid data …
Generative learning for forecasting the dynamics of high-dimensional complex systems
We introduce generative models for accelerating simulations of high-dimensional systems
through learning and evolving their effective dynamics. In the proposed Generative Learning …
through learning and evolving their effective dynamics. In the proposed Generative Learning …
Predicting physics in mesh-reduced space with temporal attention
Graph-based next-step prediction models have recently been very successful in modeling
complex high-dimensional physical systems on irregular meshes. However, due to their …
complex high-dimensional physical systems on irregular meshes. However, due to their …
Encoding physics to learn reaction–diffusion processes
Modelling complex spatiotemporal dynamical systems, such as reaction–diffusion
processes, which can be found in many fundamental dynamical effects in various …
processes, which can be found in many fundamental dynamical effects in various …