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Found 14 papers, 9 papers with code
Inpainting Computational Fluid Dynamics with Deep Learning
Fluid data completion is a research problem with high potential benefit for both experimental and computational fluid dynamics.
Latent Neural PDE Solver: a reduced-order modelling framework for partial differential equations
Neural networks have shown promising potential in accelerating the numerical simulation of systems governed by partial differential equations (PDEs).
Pretraining Strategy for Neural Potentials
We propose a mask pretraining method for Graph Neural Networks (GNNs) to improve their performance on fitting potential energy surfaces, particularly in water systems.
Hyena Neural Operator for Partial Differential Equations
The Hyena operator is an operation that enjoys sub-quadratic complexity and state space model to parameterize long convolution that enjoys a global receptive field.
Scalable Transformer for PDE Surrogate Modeling
These sub-functions are then evaluated and used to compute the instance-based kernel with an axial factorized scheme.
Physics Informed Token Transformer for Solving Partial Differential Equations
Solving Partial Differential Equations (PDEs) is the core of many fields of science and engineering.
Denoise Pretraining on Nonequilibrium Molecules for Accurate and Transferable Neural Potentials
These results highlight the potential for leveraging denoise pretraining approaches to build more generalizable neural potentials for complex molecular systems.
A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstruction
Machine learning models are gaining increasing popularity in the domain of fluid dynamics for their potential to accelerate the production of high-fidelity computational fluid dynamics data.
Graph Neural Networks for Molecules
Graph neural networks (GNNs), which are capable of learning representations from graphical data, are naturally suitable for modeling molecular systems.
Transformer for Partial Differential Equations' Operator Learning
Data-driven learning of partial differential equations' solution operators has recently emerged as a promising paradigm for approximating the underlying solutions.
Graph Neural Networks Accelerated Molecular Dynamics
Molecular Dynamics (MD) simulation is a powerful tool for understanding the dynamics and structure of matter.
Prototype memory and attention mechanisms for few shot image generation
To test our proposal, we show in a few-shot image generation task, that having a prototype memory during attention can improve image synthesis quality, learn interpretable visual concept clusters, as well as improve the robustness of the model.
TPU-GAN: Learning temporal coherence from dynamic point cloud sequences
Our model, Temporal Point cloud Upsampling GAN (TPU-GAN), can implicitly learn the underlying temporal coherence from point cloud sequence, which in turn guides the generator to produce temporally coherent output.
Learning Lagrangian Fluid Dynamics with Graph Neural Networks
We present a data-driven model for fluid simulation under Lagrangian representation.
