Physics-Informed Neural Networks (PINNs) can be regarded as general-purpose PDE solvers, but it might be slow to train PINNs on particular problems, and there is no theoretical guarantee of corresponding error bounds. In this manuscript, we propose a variant called Prior Dictionary based Physics-Informed Neural Networks (PD-PINNs). Equipped with task-dependent dictionaries, PD-PINNs enjoy enhanced representation power on the tasks, which helps to capture features provided by dictionaries so that the proposed neural networks can achieve faster convergence in the process of training. In various numerical simulations, compared with existing PINN methods, combining prior dictionaries can significantly enhance convergence speed. In terms of theory, we obtain the error bounds applicable to PINNs and PD-PINNs for solving elliptic partial differential equations of second order. It is proved that under certain mild conditions, the prediction error made by neural networks can be bounded by expected loss of PDEs and boundary conditions.
Accelerating physics-informed neural networks for full waveform inversion using a hybrid quantum-classical finite-basis architecture
Department of Geophysics, Colorado School of Mines（地质学系，科罗拉多矿业学院）；Department of Energy Science and Engineering, Stanford University（能源科学与工程系，斯坦福大学）；Department of Petroleum Engineering, Colorado School of Mines（石油工程系，科罗拉多矿业学院）
PALQO: Physics-informed Model for Accelerating Large-scale Quantum Optimization
Center on Frontiers of Computing Studies, and School of Computer Science, Peking University（前沿计算研究中心，计算机科学学院，北京大学）；Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China（电子科技大学基础与前沿科学学院）；College of Computing and Data Science Nanyang Technological University（南洋理工大学计算与数据科学学院）；School of Physical and Mathematical Sciences Nanyang Technological University（南洋理工大学物理与数学科学学院）；Department of Physics, Fudan University（复旦大学物理系）
An Advanced Physics-Informed Neural Operator for Comprehensive Design Optimization of Highly-Nonlinear Systems: An Aerospace Composites Processing Case Study
FastLRNR and Sparse Physics Informed Backpropagation
Telepix；Department of Computer Science, Arizona State University（亚利桑那州立大学计算机科学系）；School of Computing, Korea Advanced Institute of Science and Technology（韩国科学技术院计算机科学学院）；Department of Mathematics, Washington University in St. Louis（圣路易斯华盛顿大学数学系）；Department of Mathematics, University of Central Florida（中央佛罗里达大学数学系）
DOF: Accelerating High-order Differential Operators with Forward Propagation
National Key Laboratory of General Artificial Intelligence, Peking University（北京大学通用人工智能国家重点实验室）；California Institute of Technology（加州理工学院）；Stanford University（斯坦福大学）；Center for Machine Learning Research, Peking University（北京大学机器学习研究中心）
Revisiting Heat Flux Analysis of Tungsten Monoblock Divertor on EAST using Physics-Informed Neural Network
School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；Tongling University（铜陵大学）；Institute of Plasma Physics, Chinese Academy of Sciences（中国科学院等离子体物理研究所）
Texas State University（德克萨斯州立大学）；University of Minho（米尼奥大学）；University of Colorado Boulder（科罗拉多大学博尔德分校）；Texas A&M University（德克萨斯农工大学）
Recent breakthroughs in computing power have made it feasible to use machine learning and deep learning to advance scientific computing in many fields, including fluid mechanics, solid mechanics, materials science, etc. Neural networks, in particular, play a central role in this hybridization. Due to their intrinsic architecture, conventional neural networks cannot be successfully trained and scoped when data is sparse, which is the case in many scientific and engineering domains. Nonetheless, neural networks provide a solid foundation to respect physics-driven or knowledge-based constraints d…
University of Maryland（马里兰大学）；The Hong Kong Polytechnic University（香港理工大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
In this paper, we leverage the recent advances in physics-informed neural network (PINN) and develop a generic PINN-based framework to assess the reliability of multi-state systems (MSSs). The proposed methodology consists of two major steps. In the first step, we recast the reliability assessment of MSS as a machine learning problem using the framework of PINN. A feedforward neural network with two individual loss groups are constructed to encode the initial condition and state transitions governed by ordinary differential equations (ODEs) in MSS. Next, we tackle the problem of high imbalance…
Physics-Informed Stochastic Configuration Machine: A Backpropagation-Free Neural Network with Fast Training for Nonlinear Differential Equations
School of Automation, Central South University（中南大学自动化学院）；Johns Hopkins University（约翰斯·霍普金斯大学）；China Institute of Water Resources and Hydropower Research（中国水利水电科学研究院）
PIP$^2$ Net: Physics-informed Partition Penalty Deep Operator Network
Hongjin Mi School of Mathematics Shanghai University of Finance and Economics（Hongjin Mi 数学学院 上海金融学院）；Huiqiang Lun Faculty of Liberal Arts and Professional Studies York University（Huiqiang Lun 文科与专业学院 约克大学）；Changhong Mou Department of Mathematics and Statistics Utah State University（Changhong Mou 数学与统计学系 Utah州立大学）；Yeyu Zhang School of Mathematics Shanghai University of Finance and Economics（Yeyu Zhang 数学学院 上海金融学院）
Accelerating 4D Hyperspectral Imaging through Physics-Informed Neural Representation and Adaptive Sampling
Dept. of Electrical and Computer Engineering, UC San Diego（加州大学圣地亚哥分校电气与计算机工程系）；Dept. of Chemistry, UC San Diego（加州大学圣地亚哥分校化学系）；Materials Science and Engineering Program, UC San Diego（加州大学圣地亚哥分校材料科学与工程项目）
Shape Derivative-Informed Neural Operators with Application to Risk-Averse Shape Optimization
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, USA（奥登计算工程与科学研究所，德克萨斯大学奥斯汀分校，奥斯汀，德克萨斯，美国）；School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia（数学科学学院，昆士兰科技大学，布里斯班，澳大利亚）；Centre for Data Science, Queensland University of Technology, Brisbane, Australia（数据科学中心，昆士兰科技大学，布里斯班，澳大利亚）；Department of Mathematics, The Ohio State University, Columbus, Ohio, USA（数学系，俄亥俄州立大学，哥伦布，俄亥俄，美国）；Department of Engineering Science and Biomedical Engineering, University of Auckland, Auckland, New Zealand（工程科学与生物医学工程系，奥克兰大学，奥克兰，新西兰）；Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas, USA（沃克机械工程系，德克萨斯大学奥斯汀分校，奥斯汀，德克萨斯，美国）
Center for Artificial Intelligence Research (CAiRE)（人工智能研究中心（CAiRE））；The Hong Kong University of Science and Technology（香港科技大学）
Recently, data-driven task-oriented dialogue systems have achieved promising performance in English. However, developing dialogue systems that support low-resource languages remains a long-standing challenge due to the absence of high-quality data. In order to circumvent the expensive and time-consuming data collection, we introduce Attention-Informed Mixed-Language Training (MLT), a novel zero-shot adaptation method for cross-lingual task-oriented dialogue systems. It leverages very few task-related parallel word pairs to generate code-switching sentences for learning the inter-lingual semant…
Accelerating MRI with Longitudinally-informed Latent Posterior Sampling
Electrical Engineering , Stanford University , California , USA（电气工程，斯坦福大学，加利福尼亚，美国）；Radiology , Stanford University , California , USA（放射学，斯坦福大学，加利福尼亚，美国）
Residual-based physics-informed transfer learning: A hybrid method for accelerating long-term CFD simulations via deep learning
KTH Royal Institute of Technology（KTH皇家理工学院）；Jeonbuk National University（国立全北大学）；Hanyang University（汉阳大学）；Institute of Nano Science & Technology, Hanyang University（汉阳大学纳米科学与技术研究所）
LaPON: A Lagrange's-mean-value-theorem-inspired operator network for solving PDEs and its application on NSE
Ocean College, Zhejiang University（浙江大学海洋学院）；Ocean Research Center of Zhoushan, Zhejiang University（浙江大学舟山海洋研究中心）；Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新研究院）
Massachusetts Institute of Technology（麻省理工学院）；Julia Computing（Julia Computing公司）；Mitsubishi Electric Research Laboratories（三菱电机研究实验室）
Modern design, control, and optimization often requires simulation of highly nonlinear models, leading to prohibitive computational costs. These costs can be amortized by evaluating a cheap surrogate of the full model. Here we present a general data-driven method, the continuous-time echo state network (CTESN), for generating surrogates of nonlinear ordinary differential equations with dynamics at widely separated timescales. We empirically demonstrate near-constant time performance using our CTESNs on a physically motivated scalable model of a heating system whose full execution time increase…
Accelerating lensed quasar discovery and modeling with physics-informed variational autoencoders
Technical University of Munich（慕尼黑工业大学）；Max-Planck-Institut für Astrophysik（马克斯·普朗克天体物理研究所）；Università degli Studi di Milano（米兰大学）；INAF – IASF Milano（意大利国家天体物理研究所米兰分部）；Academia Sinica Institute of Astronomy and Astrophysics (ASIAA)（中央研究院天文及天文物理研究所）；Aix-Marseille Université（艾克斯-马赛大学）；American Museum of Natural History（美国自然历史博物馆）；Lehman College of the CUNY（纽约城市大学莱曼学院）
本文提出物理信息变分自编码器模型 VariLens，可在单 CPU 毫秒级完成透镜分类与 SIE 参数建模，并从约8000万源中筛选出42个待光谱确认的透镜类星体候选体。
Accelerating Natural Gradient Descent for PINNs with Randomized Numerical Linear Algebra
University of Pavia（帕维亚大学）；Department of Mathematics（数学系）；Department of Civil Engineering and Architecture（土木工程与建筑系）；Institut Camille Jordan, Lyon 1 Université（让·乔丹研究所，里昂1大学）
Advanced Micro Devices, Inc. (AMD)（超威半导体公司（AMD））；Sandia National Laboratories（桑迪亚国家实验室）
This work presents a physics-informed deep learning-based super-resolution framework to enhance the spatio-temporal resolution of the solution of time-dependent partial differential equations (PDE). Prior works on deep learning-based super-resolution models have shown promise in accelerating engineering design by reducing the computational expense of traditional numerical schemes. However, these models heavily rely on the availability of high-resolution (HR) labeled data needed during training. In this work, we propose a physics-informed deep learning-based framework to enhance the spatial and…
Universidad de Valladolid（巴利亚多利德大学）；King’s College London（伦敦国王学院）；Instituto Superior Técnico, Universidade de Lisboa（里斯本大学高等技术学院）；Centre for Marine Sciences - CCMAR（海洋科学中心（CCMAR））
First-pass perfusion cardiac magnetic resonance (FPP-CMR) is becoming an essential non-invasive imaging method for detecting deficits of myocardial blood flow, allowing the assessment of coronary heart disease. Nevertheless, acquisitions suffer from relatively low spatial resolution and limited heart coverage. Compressed sensing (CS) methods have been proposed to accelerate FPP-CMR and achieve higher spatial resolution. However, the long reconstruction times have limited the widespread clinical use of CS in FPP-CMR. Deep learning techniques based on supervised learning have emerged as alternat…
Quality or Quantity? Error-Informed Selective Online Learning with Gaussian Processes in Multi-Agent Systems: Extended Version
Technical University of Munich (TUM)（慕尼黑技术大学）；School of Computation, Information and Technology (CIT)（计算、信息与技术学院）；College of Intelligent Systems Science and Engineering（智能系统科学与工程学院）；School of Electrical and Mechanical Engineering（电气与机械工程学院）；Research and Innovation Centre（研究与创新中心）；University of Adelaide（阿德莱德大学）；Obuda University（奥布达大学）
