Latent Generative Solvers for Generalizable Long-Term Physics Simulation
cs.AI, cs.LG
Submitted: 2026-02-11
Updated: 2026-09-25
Terminology
Sources
- What Regularized Auto-Encoders Learn from the Data Generating Distribution
- Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation
- VICON: Vision In-Context Operator Networks for Multi-Physics Fluid Dynamics Prediction
- Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion
- FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning
- Auto-Regressive Moving Diffusion Models for Time Series Forecasting
- Ca2-VDM: Efficient Autoregressive Video Diffusion Model with Causal Generation and Cache Sharing
- Towards Multi-spatiotemporal-scale Generalized PDE Modeling
- DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training
- Wavelet Diffusion Neural Operator
- DiffusionPDE: Generative PDE-Solving Under Partial Observation
- Perceiver: General Perception with Iterative Attention
- Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders
- Back to Basics: Let Denoising Generative Models Denoise
- Transformer for Partial Differential Equations' Operator Learning
- Fourier Neural Operator for Parametric Partial Differential Equations
- PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers
- PROSE-FD: A Multimodal PDE Foundation Model for Learning Multiple Operators for Forecasting Fluid Dynamics
- A ConvNet for the 2020s
- SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers
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