Physics-aligned Schrödinger bridge
Fuente:
arXiv
Saved in:
| Main Authors: | Li, Zeyu, Dou, Hongkun, Fang, Shen, Han, Wang, Deng, Yue, Yang, Lijun |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics
by: Zhang, Rui, et al.
Published: (2025)
by: Zhang, Rui, et al.
Published: (2025)
Generative prediction of flow fields around an obstacle using the diffusion model
by: Hu, Jiajun, et al.
Published: (2024)
by: Hu, Jiajun, et al.
Published: (2024)
FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulation
by: Wang, Haixin, et al.
Published: (2025)
by: Wang, Haixin, et al.
Published: (2025)
Data assimilation and parameter identification for water waves using the nonlinear Schrödinger equation and physics-informed neural networks
by: Ehlers, Svenja, et al.
Published: (2024)
by: Ehlers, Svenja, et al.
Published: (2024)
Physical Fidelity Reconstruction via Improved Consistency-Distilled Flow Matching for Dynamical Systems
by: Ma, Sicheng, et al.
Published: (2026)
by: Ma, Sicheng, et al.
Published: (2026)
Physics-informed deep-learning applications to experimental fluid mechanics
by: Eivazi, Hamidreza, et al.
Published: (2022)
by: Eivazi, Hamidreza, et al.
Published: (2022)
Physics-integrated neural differentiable modeling for immersed boundary systems
by: Li, Chenglin, et al.
Published: (2026)
by: Li, Chenglin, et al.
Published: (2026)
Solving Euler equations with Multiple Discontinuities via Separation-Transfer Physics-Informed Neural Networks
by: Wang, Chuanxing, et al.
Published: (2025)
by: Wang, Chuanxing, et al.
Published: (2025)
Physics-Informed Chebyshev Polynomial Neural Operator for Parametric Partial Differential Equations
by: Chen, Biao, et al.
Published: (2026)
by: Chen, Biao, et al.
Published: (2026)
Hard Constraint Projection in a Physics Informed Neural Network
by: Horne, Miranda J. S., et al.
Published: (2026)
by: Horne, Miranda J. S., et al.
Published: (2026)
DiffFluid: Plain Diffusion Models are Effective Predictors of Flow Dynamics
by: Luo, Dongyu, et al.
Published: (2024)
by: Luo, Dongyu, et al.
Published: (2024)
Coupled Integral PINN for Discontinuity
by: Wang, Yeping, et al.
Published: (2024)
by: Wang, Yeping, et al.
Published: (2024)
Learning with Physical Constraints
by: Mendez, Miguel A., et al.
Published: (2025)
by: Mendez, Miguel A., et al.
Published: (2025)
The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning
by: Ohana, Ruben, et al.
Published: (2024)
by: Ohana, Ruben, et al.
Published: (2024)
CFDTwin: An open-source GUI and Python toolkit for POD-NN surrogate modeling of ANSYS Fluent simulations
by: Curl, Daniel, et al.
Published: (2026)
by: Curl, Daniel, et al.
Published: (2026)
Towards LLM-enabled autonomous combustion research: A literature-aware agent for self-corrective modeling workflows
by: Xiao, Ke, et al.
Published: (2026)
by: Xiao, Ke, et al.
Published: (2026)
Stabilizing the Maximal Entropy Moment Method for Rarefied Gas Dynamics at Single-Precision
by: Zheng, Candi, et al.
Published: (2023)
by: Zheng, Candi, et al.
Published: (2023)
Machine-learning-based multipoint optimization of fluidic injection parameters for improving nozzle performance
by: Yang, Yunjia, et al.
Published: (2024)
by: Yang, Yunjia, et al.
Published: (2024)
Physics-Guided Machine Learning for Uncertainty Quantification in Turbulence Models
by: Chu, Minghan, et al.
Published: (2025)
by: Chu, Minghan, et al.
Published: (2025)
Geometric and Physical Constraints Synergistically Enhance Neural PDE Surrogates
by: Huang, Yunfei, et al.
Published: (2025)
by: Huang, Yunfei, et al.
Published: (2025)
Vector-based loss functions for turbulent flow field inpainting
by: Baker, Samuel J., et al.
Published: (2025)
by: Baker, Samuel J., et al.
Published: (2025)
Data-Driven Computing Methods for Nonlinear Physics Systems with Geometric Constraints
by: Tong, Yunjin
Published: (2024)
by: Tong, Yunjin
Published: (2024)
A Physics-Informed Spatiotemporal Deep Learning Framework for Turbulent Systems
by: Menicali, Luca, et al.
Published: (2025)
by: Menicali, Luca, et al.
Published: (2025)
MeshMask: Physics-Based Simulations with Masked Graph Neural Networks
by: Garnier, Paul, et al.
Published: (2025)
by: Garnier, Paul, et al.
Published: (2025)
Rapid aerodynamic prediction of swept wings via physics-embedded transfer learning
by: Yang, Yunjia, et al.
Published: (2024)
by: Yang, Yunjia, et al.
Published: (2024)
Lost in Latent Space: An Empirical Study of Latent Diffusion Models for Physics Emulation
by: Rozet, François, et al.
Published: (2025)
by: Rozet, François, et al.
Published: (2025)
Non-intrusive Learning of Physics-Informed Spatio-temporal Surrogate for Accelerating Design
by: Mondal, Sudeepta, et al.
Published: (2026)
by: Mondal, Sudeepta, et al.
Published: (2026)
Finding the Underlying Viscoelastic Constitutive Equation via Universal Differential Equations and Differentiable Physics
by: Rodrigues, Elias C., et al.
Published: (2024)
by: Rodrigues, Elias C., et al.
Published: (2024)
Physics-constrained coupled neural differential equations for one dimensional blood flow modeling
by: Csala, Hunor, et al.
Published: (2024)
by: Csala, Hunor, et al.
Published: (2024)
Physics-constrained convolutional neural networks for inverse problems in spatiotemporal partial differential equations
by: Kelshaw, Daniel, et al.
Published: (2024)
by: Kelshaw, Daniel, et al.
Published: (2024)
NeuralFoil: An Airfoil Aerodynamics Analysis Tool Using Physics-Informed Machine Learning
by: Sharpe, Peter, et al.
Published: (2025)
by: Sharpe, Peter, et al.
Published: (2025)
Project and Generate: Divergence-Free Neural Operators for Incompressible Flows
by: Li, Xigui, et al.
Published: (2026)
by: Li, Xigui, et al.
Published: (2026)
Shock-Aware Physics-Guided Fusion-DeepONet Operator for Rarefied Micro-Nozzle Flows
by: Roohi, Ehsan, et al.
Published: (2025)
by: Roohi, Ehsan, et al.
Published: (2025)
Impact of Loss Weight and Model Complexity on Physics-Informed Neural Networks for Computational Fluid Dynamics
by: Chou, Yi En, et al.
Published: (2025)
by: Chou, Yi En, et al.
Published: (2025)
Physics-Informed Graph Neural Network Surrogates for Turbulent Nanoparticle Dispersion in Dental Clinical Environments
by: Shende, Takshak, et al.
Published: (2026)
by: Shende, Takshak, et al.
Published: (2026)
Physics-informed neural networks for hidden boundary detection and flow field reconstruction
by: Zhu, Yongzheng, et al.
Published: (2025)
by: Zhu, Yongzheng, et al.
Published: (2025)
Challenges and Advancements in Modeling Shock Fronts with Physics-Informed Neural Networks: A Review and Benchmarking Study
by: Abbasi, Jassem, et al.
Published: (2025)
by: Abbasi, Jassem, et al.
Published: (2025)
Physics-constrained DeepONet for Surrogate CFD models: a curved backward-facing step case
by: Jnini, Anas, et al.
Published: (2025)
by: Jnini, Anas, et al.
Published: (2025)
Physics-Based Machine Learning Closures and Wall Models for Hypersonic Transition-Continuum Boundary Layer Predictions
by: Nair, Ashish S., et al.
Published: (2025)
by: Nair, Ashish S., et al.
Published: (2025)
CoNFiLD-inlet: Synthetic Turbulence Inflow Using Generative Latent Diffusion Models with Neural Fields
by: Liu, Xin-Yang, et al.
Published: (2024)
by: Liu, Xin-Yang, et al.
Published: (2024)
Similar Items
-
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics
by: Zhang, Rui, et al.
Published: (2025) -
Generative prediction of flow fields around an obstacle using the diffusion model
by: Hu, Jiajun, et al.
Published: (2024) -
FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulation
by: Wang, Haixin, et al.
Published: (2025) -
Data assimilation and parameter identification for water waves using the nonlinear Schrödinger equation and physics-informed neural networks
by: Ehlers, Svenja, et al.
Published: (2024) -
Physical Fidelity Reconstruction via Improved Consistency-Distilled Flow Matching for Dynamical Systems
by: Ma, Sicheng, et al.
Published: (2026)