Surface Flux Transport Modeling using Physics Informed Neural Networks
Indian Institute of Technology Indore（印度理工学院印多尔分校）；University of Bath（巴斯大学）；Bay Area Environmental Research Institute（湾区环境研究所）；Lockheed Martin Solar and Astrophysics Laboratory（洛克希德·马丁太阳与天体物理实验室）；CSIRO（联邦科学与工业研究组织）
Jithu J Athalathil, Bhargav Vaidya, Sayan Kundu, Vishal Upendran, Mark C. M. Cheung
Studying the magnetic field properties on the solar surface is crucial for understanding the solar and heliospheric activities, which in turn shape space weather in the solar system. Surface Flux Transport (SFT) modeling helps us to simulate and analyse the transport and evolution of magnetic flux on the solar surface, providing valuable insights into the mechanisms responsible for solar activity. In this work, we demonstrate the use of machine learning techniques in solving magnetic flux transport, making it accurate. We have developed a novel Physics-Informed Neural Networks (PINN)-based model to study the evolution of Bipolar Magnetic Regions (BMRs) using SFT in one-dimensional azimuthally averaged and also in two-dimensions. We demonstrate the efficiency and computational feasibility of our PINN-based model by comparing its performance and accuracy with that of a numerical model implemented using the Runge-Kutta Implicit-Explicit (RK-IMEX) scheme. The mesh-independent PINN method can be used to reproduce the observed polar magnetic field with better flux conservation. This advancement is important for accurately reproducing observed polar magnetic fields, thereby providing insights into the strength of future solar cycles. This work paves the way for more efficient and accurate simulations of solar magnetic flux transport and showcases the applicability of PINN in solving advection-diffusion equations with a particular focus on heliophysics.
Convex Physics Informed Neural Networks for the Monge-Ampère Optimal Transport Problem
Geneva School of Business Administration (HEG), University of Applied Sciences；Institute of Mathematics, Ecole Polytechnique F\'ed\'erale de Lausanne, 1015 Lausanne, Switzerland, Email
Joint Initialization of Flux Networks and Effective Multiplication Factor for Physics-Informed Neural Networks Solving Neutron Diffusion Problems
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications（重庆邮电大学计算机科学与技术学院）；Center for Scientific Intelligence Innovation, University of Science and Technology of China（中国科学技术大学科学智能创新中心）；Institute of Advanced Technology, University of Science and Technology of China（中国科学技术大学先进技术研究院）
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）
Quantitative Analysis of Molecular Transport in the Extracellular Space Using Physics-Informed Neural Network
Tsinghua University（清华大学）；Peking University Health Science Center（北京大学医学部）；Fudan University（复旦大学）；Xiamen University（厦门大学）；Peking University Third Hospital（北京大学第三医院）
Physics-Informed Neural Network Modeling of Biodegradable Contaminant Transport through GCL/SL Composite Liners
Department of Civil, Environmental, and Infrastructure Engineering, George Mason University（乔治·马歇尔大学土木、环境与基础设施工程系）；State Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences（中国科学院寒区工程与冻土科学联合实验室，西北生态环境资源研究院）；Laboratoire Navier/CERMES, École Nationale des Ponts et Chaussées, Institut Polytechnique de Paris（巴黎理工学院劳达实验室/塞姆斯实验室，法国国家桥梁与道路学院）
Physics-informed neural networks need a physicist to be accurate: the case of mass and heat transport in Fischer-Tropsch catalyst particles
SoftServe Inc.（SoftServe公司）；Taras Shevchenko National University of Kyiv（基辅塔拉斯·舍甫琴科国立大学）；Shell India Markets Pvt. Ltd.（壳牌印度市场私人有限公司）；Shell Global Solutions International B.V.（壳牌全球解决方案国际有限公司）
Physics-Informed Neural Network Surrogate for Oxygen Vacancy Dynamics in epitaxial $\mathrm{SrTiO_3}$ on Si memristors via Dynamic Spectral Optimization
Macroscopic transport patterns of UAV traffic in 3D anisotropic wind fields: A constraint-preserving hybrid PINN-FVM approach
Nanophotonics and Biophotonics Key Laboratory of Jilin Province, School of Physics, Changchun University of Science and Technology（吉林省纳米光子学与生物光子学重点实验室，物理学院，长春理工大学）
Magnetic Flux Transport and the Long-Term Evolution of Solar Active Regions
由于太阳周围有多个有利位置，STEREO和SDO成像观测提供了连续观察太阳表面的独特机会。我们使用这些天文台的He II 304 A数据来分离和跟踪10个活动区域，并研究它们的长期演化。我们发现，活跃地区通常遵循一个标准的模式出现了几天，然后是一个较慢的衰减，在时间上成比例的峰值强度在该地区。由于STEREO不直接观测磁场，我们采用通量-光度关系来推断总的无符号磁通量演变。要调查这种磁通量衰减几个旋转，我们使用的表面通量传输模型，平流通量传输（AFT）模型，模拟对流使用随时间变化的速度场，并发现该模型提供了现实的预测时，有关活动区域的磁场强度和分布在峰值通量的信息是可用的。最后，我们说明了如何304位的图像可以用作磁通量测量时，磁场数据是不可访问的代理。
Active-region Modulation of Subsurface Meridional Flows and Magnetic Flux Transport on the Sun
Indian Institute of Astrophysics（印度天体物理研究所）；Pondicherry University（本地治里大学）；W. W. Hansen Experimental Physics Laboratory, Stanford University（斯坦福大学W.W.汉森实验物理实验室）；National Solar Observatory（国家太阳天文台）
