Spatiotemporal Field Generation Based on Hybrid Mamba-Transformer with Physics-informed Fine-tuning

• URL: http://arxiv.org/abs/2505.11578v4
• Date: Fri, 13 Jun 2025 14:02:43 GMT
• Title: Spatiotemporal Field Generation Based on Hybrid Mamba-Transformer with Physics-informed Fine-tuning
• Authors: Peimian Du, Jiabin Liu, Xiaowei Jin, Wangmeng Zuo, Hui Li,
• Abstract summary: This research confronts the challenge of substantial physical equation discrepancies in the generation of physical fields through trained models.<n>A physical field generation model, named HMT-PF, is developed based on the hybrid Mamba-Transformer architecture.
• Score: 46.67399627400437
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This research confronts the challenge of substantial physical equation discrepancies encountered in the generation of spatiotemporal physical fields through data-driven trained models. A spatiotemporal physical field generation model, named HMT-PF, is developed based on the hybrid Mamba-Transformer architecture, incorporating unstructured grid information as input. A fine-tuning block, enhanced with physical information, is introduced to effectively reduce the physical equation discrepancies. The physical equation residuals are computed through a point query mechanism for efficient gradient evaluation, then encoded into latent space for refinement. The fine-tuning process employs a self-supervised learning approach to achieve physical consistency while maintaining essential field characteristics. Results show that the hybrid Mamba-Transformer model achieves good performance in generating spatiotemporal fields, while the physics-informed fine-tuning mechanism further reduces significant physical errors effectively. A MSE-R evaluation method is developed to assess the accuracy and realism of physical field generation.

Related papers

• Hybrid Generative Modeling for Incomplete Physics: Deep Grey-Box Meets Optimal Transport [48.06072022424773]
  Many real-world systems are described only approximately with missing or unknown terms in the equations.<n>This makes the distribution of the physics model differ from the true data-generating process (DGP)<n>We present a novel hybrid generative model approach combining deep grey-box modelling with Optimal Transport (OT) methods to enhance incomplete physics models.
  arXiv  Detail & Related papers  (2025-06-27T13:23:27Z)
• Physics-Embedded Neural Networks for sEMG-based Continuous Motion Estimation [3.606446851103922]
  sEMG-based motion estimation methods often rely on subject-specific musculoskeletal (MSK) models that are difficult to calibrate.<n>This paper introduces a novel Physics-Embedded Neural Network (PENN) that combines interpretable MSK forward-dynamics with data-driven residual learning.
  arXiv  Detail & Related papers  (2025-06-17T16:07:20Z)
• Flow Matching Meets PDEs: A Unified Framework for Physics-Constrained Generation [21.321570407292263]
  We propose Physics-Based Flow Matching, a generative framework that embeds physical constraints, both PDE residuals and algebraic relations, into the flow matching objective.<n>We show that our approach yields up to an $8times$ more accurate physical residuals compared to FM, while clearly outperforming existing algorithms in terms of distributional accuracy.
  arXiv  Detail & Related papers  (2025-06-10T09:13:37Z)
• Fine-Tuning Hybrid Physics-Informed Neural Networks for Vehicle Dynamics Model Estimation [2.432448600920501]
  This paper introduces the Fine-Tuning Hybrid Dynamics (FTHD) method, which integrates supervised and unsupervised Physics-Informed Neural Networks (PINNs) FTHD fine-tunes a pre-trained Deep Dynamics Model (DDM) using a smaller training dataset, delivering superior performance compared to state-of-the-art methods. An Extended Kalman Filter (EKF) is embedded within FTHD to effectively manage noisy real-world data, ensuring accurate denoising. Results demonstrate that the hybrid approach significantly improves parameter estimation accuracy, even with reduced data, and outperforms existing models.
  arXiv  Detail & Related papers  (2024-09-29T10:33:07Z)
• PhyRecon: Physically Plausible Neural Scene Reconstruction [81.73129450090684]
  We introduce PHYRECON, the first approach to leverage both differentiable rendering and differentiable physics simulation to learn implicit surface representations. Central to this design is an efficient transformation between SDF-based implicit representations and explicit surface points. Our results also exhibit superior physical stability in physical simulators, with at least a 40% improvement across all datasets.
  arXiv  Detail & Related papers  (2024-04-25T15:06:58Z)
• Discovering Interpretable Physical Models using Symbolic Regression and Discrete Exterior Calculus [55.2480439325792]
  We propose a framework that combines Symbolic Regression (SR) and Discrete Exterior Calculus (DEC) for the automated discovery of physical models. DEC provides building blocks for the discrete analogue of field theories, which are beyond the state-of-the-art applications of SR to physical problems. We prove the effectiveness of our methodology by re-discovering three models of Continuum Physics from synthetic experimental data.
  arXiv  Detail & Related papers  (2023-10-10T13:23:05Z)
• Physics-Driven Turbulence Image Restoration with Stochastic Refinement [80.79900297089176]
  Image distortion by atmospheric turbulence is a critical problem in long-range optical imaging systems. Fast and physics-grounded simulation tools have been introduced to help the deep-learning models adapt to real-world turbulence conditions. This paper proposes the Physics-integrated Restoration Network (PiRN) to help the network to disentangle theity from the degradation and the underlying image.
  arXiv  Detail & Related papers  (2023-07-20T05:49:21Z)
• Physics-informed UNets for Discovering Hidden Elasticity in Heterogeneous Materials [0.0]
  We develop a novel UNet-based neural network model for inversion in elasticity (El-UNet) We show superior performance, both in terms of accuracy and computational cost, by El-UNet compared to fully-connected physics-informed neural networks.
  arXiv  Detail & Related papers  (2023-06-01T23:35:03Z)
• Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling [86.9726984929758]
  We focus on the integration of incomplete physics models into deep generative models. We propose a VAE architecture in which a part of the latent space is grounded by physics. We demonstrate generative performance improvements over a set of synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2021-02-25T20:28:52Z)
• Fast Gravitational Approach for Rigid Point Set Registration with Ordinary Differential Equations [79.71184760864507]
  This article introduces a new physics-based method for rigid point set alignment called Fast Gravitational Approach (FGA) In FGA, the source and target point sets are interpreted as rigid particle swarms with masses interacting in a globally multiply-linked manner while moving in a simulated gravitational force field. We show that the new method class has characteristics not found in previous alignment methods.
  arXiv  Detail & Related papers  (2020-09-28T15:05:39Z)
• Physics-Consistent Data-driven Waveform Inversion with Adaptive Data Augmentation [12.564534712461331]
  We develop a new hybrid computational approach to solve full-waveform inversion (FWI) We develop a data augmentation strategy that can improve the representativity of the training set. We apply our method to synthetic elastic seismic waveform data generated from a subsurface geologic model built on a carbon sequestration site at Kimberlina, California.
  arXiv  Detail & Related papers  (2020-09-03T17:12:55Z)
• Variational Autoencoding of PDE Inverse Problems [12.716429755564821]
  Modern machine learning allows one to circumvent problems involving prior knowledge and physical laws. In this work we fold the mechanistic model into a flexible data-driven surrogate to arrive at a physically structured decoder network. We employ the variational form of the PDE problem and introduce local approximations as a form of model based data augmentation.
  arXiv  Detail & Related papers  (2020-06-28T16:17:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.