We delve into the physics-informed neural reconstruction of smoke and obstacles through sparse-view RGB videos, tackling challenges arising from limited observation of complex dynamics. Existing physics-informed neural networks often emphasize short-term physics constraints, leaving the proper preservation of long-term conservation less explored. We introduce Neural Characteristic Trajectory Fields, a novel representation utilizing Eulerian neural fields to implicitly model Lagrangian fluid trajectories. This topology-free, auto-differentiable representation facilitates efficient flow map calculations between arbitrary frames as well as efficient velocity extraction via auto-differentiation. Consequently, it enables end-to-end supervision covering long-term conservation and short-term physics priors. Building on the representation, we propose physics-informed trajectory learning and integration into NeRF-based scene reconstruction. We enable advanced obstacle handling through self-supervised scene decomposition and seamless integrated boundary constraints. Our results showcase the ability to overcome challenges like occlusion uncertainty, density-color ambiguity, and static-dynamic entanglements. Code and sample tests are at \url{https://github.com/19reborn/PICT_smoke}.
Physics Informed Neural Fields for Smoke Reconstruction with Sparse Data
Max Planck Institute for Informatics, SIC（马克斯·普朗克信息学研究所SIC）；Institute of Software Chinese Academy of Sciences（中国科学院软件研究所）；Technical University of Munich（慕尼黑工业大学）
Deep learning-based surrogate modeling is becoming a promising approach for learning and simulating dynamical systems. Deep-learning methods, however, find very challenging learning stiff dynamics. In this paper, we develop DAE-PINN, the first effective deep-learning framework for learning and simulating the solution trajectories of nonlinear differential-algebraic equations (DAE), which present a form of infinite stiffness and describe, for example, the dynamics of power networks. Our DAE-PINN bases its effectiveness on the synergy between implicit Runge-Kutta time-stepping schemes (designed …
Solving a class of stochastic optimal control problems by physics-informed neural networks
School of Mathematics and Statistics（数学与统计学学院）；MOE Key Laboratory for Complexity Science in Aerospace（复杂科学在航空航天中的关键实验室）；State Key Laboratory of Fluid Power and Mechatronic Systems, Department of Mechanics（流体动力与机械系统国家重点实验室，力学系）
Integrating Physics-Informed Neural Networks for Safe Reinforcement Learning in a 1-DoF Helicopter System
Josef Ressel Centre for Intelligent and Secure Industrial Automation, Salzburg University of Applied Sciences（约瑟夫·雷塞尔智能与安全工业自动化中心，萨尔茨堡应用科学大学）；Salzburg University of Applied Sciences（萨尔茨堡应用科学大学）；Paris Lodron University of Salzburg（萨尔茨堡帕里斯·洛德隆大学）
The spread of many infectious diseases is modeled using variants of the SIR compartmental model, which is a coupled differential equation. The coefficients of the SIR model determine the spread trajectories of disease, on whose basis proactive measures can be taken. Hence, the coefficient estimates must be both fast and accurate. Shaier et al. in the paper "Disease Informed Neural Networks" used Physics Informed Neural Networks (PINNs) to estimate the parameters of the SIR model. There are two drawbacks to this approach. First, the training time for PINNs is high, with certain diseases taking …
Magnetic Resonance Imaging (MRI) has long been considered to be among "the gold standards" of diagnostic medical imaging. The long acquisition times, however, render MRI prone to motion artifacts, let alone their adverse contribution to the relative high costs of MRI examination. Over the last few decades, multiple studies have focused on the development of both physical and post-processing methods for accelerated acquisition of MRI scans. These two approaches, however, have so far been addressed separately. On the other hand, recent works in optical computational imaging have demonstrated gro…
VLIPP: Towards Physically Plausible Video Generation with Vision and Language Informed Physical Prior
Monash University（莫纳什大学）；Dalian University of Technology（大连理工大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Oxford University（牛津大学）；The University of Sydney（悉尼大学）；ZMO AI（智源人工智能）
Event-driven dynamic trajectories reconstruction and measurement of mechanical parameters for fragments
School of Aeronautics, Northwestern Polytechnical University（航空学院，西北工业大学）；College of Aerospace Science and Engineering, National University of Defense Technology（航空科学与工程学院，国防科技大学）；College of Science, National University of Defense Technology（科学学院，国防科技大学）
