The aim of this work is to evaluate the feasibility of re-implementing some key parts of the widely used Weather Research and Forecasting WRF-SFIRE simulator by replacing its core differential equations numerical solvers with state-of-the-art physics-informed machine learning techniques to solve ODEs and PDEs, in order to transform it into a real-time simulator for wildfire spread prediction. The main programming language used is Julia, a compiled language which offers better perfomance than interpreted ones, providing Just in Time (JIT) compilation with different optimization levels. Moreover, Julia is particularly well suited for numerical computation and for the solution of complex physical models, both considering the syntax and the presence of some specific libraries such as DifferentialEquations.jl and ModellingToolkit.jl.
GraphFire-X: Physics-Informed Graph Attention Networks and Structural Gradient Boosting for Building-Scale Wildfire Preparedness at the Wildland-Urban Interface
Urban Reslience.AI Lab, Zachry Department of Civil and Environmental Engineering, Texas A&M University（Urban Reslience.AI实验室，Zachry土木与环境工程系，德克萨斯A&M大学）；Department of Computer Science and Engineering, Texas A&M University（计算机科学与工程系，德克萨斯A&M大学）
Generative Algorithms for Fusion of Physics-Based Wildfire Spread Models with Satellite Data for Initializing Wildfire Forecasts
University of Southern California（南加州大学）；University of Maryland（马里兰大学）；San Jose State University（圣何塞州立大学）；University of Colorado Denver（科罗拉多大学丹佛分校）；Colorado State University（科罗拉多州立大学）；University of Utah（犹他大学）
Physics-informed reservoir characterization from bulk and extreme pressure events with a differentiable simulator
Earth and Environmental Sciences Division, Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室地球与环境科学部）；Courant Institute School of Mathematics, Computing and Data Science, New York University（纽约大学柯朗研究所数学、计算与数据科学学院）
University of California, San Diego（加州大学圣地亚哥分校）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；University of Arizona（亚利桑那大学）
A parametric adaptive greedy Latent Space Dynamics Identification (gLaSDI) framework is developed for accurate, efficient, and certified data-driven physics-informed greedy auto-encoder simulators of high-dimensional nonlinear dynamical systems. In the proposed framework, an auto-encoder and dynamics identification models are trained interactively to discover intrinsic and simple latent-space dynamics. To effectively explore the parameter space for optimal model performance, an adaptive greedy sampling algorithm integrated with a physics-informed error indicator is introduced to search for opt…
Oregon State University（俄勒冈州立大学）；University of Notre Dame（圣母大学）
Policy analysts wish to visualize a range of policies for large simulator-defined Markov Decision Processes (MDPs). One visualization approach is to invoke the simulator to generate on-policy trajectories and then visualize those trajectories. When the simulator is expensive, this is not practical, and some method is required for generating trajectories for new policies without invoking the simulator. The method of Model-Free Monte Carlo (MFMC) can do this by stitching together state transitions for a new policy based on previously-sampled trajectories from other policies. This "off-policy Mon…
University of Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究院）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）
Climate change increases the number of extreme weather events (wind and snowstorms, heavy rains, wildfires) that compromise power system reliability and lead to multiple equipment failures. Real-time and accurate detecting of potential line failures is the first step to mitigating the extreme weather impact and activating emergency controls. Power balance equations nonlinearity, increased uncertainty in generation during extreme events, and lack of grid observability compromise the efficiency of traditional data-driven failure detection methods. At the same time, modern problem-oblivious machi…
Columbia University（哥伦比亚大学）；IBM Research（IBM研究院）；German Aerospace Center（德国航空航天中心）
The global carbon cycle is a key process to understand how our climate is changing. However, monitoring the dynamics is difficult because a high-resolution robust measurement of key state parameters including the aboveground carbon biomass (AGB) is required. Here, we use deep neural network to generate a wall-to-wall map of AGB within the Continental USA (CONUS) with 30-meter spatial resolution for the year 2021. We combine radar and optical hyperspectral imagery, with a physical climate parameter of SIF-based GPP. Validation results show that a masked variation of UNet has the lowest validati…
Gradient-Informed Temporal Sampling Improves Rollout Accuracy in PDE Surrogate Training
School of Future Technology, South China University of Technology, China（未来技术学院，华南理工大学，中国）；State Key Laboratory of Ocean Sensing & Ocean College, Zhejiang University, China（海洋感知国家重点实验室及海洋学院，浙江大学，中国）；Kavli Institute for Astrophysics and Space Research, Massachusetts Institute of Technology, USA（天体物理与空间研究所，麻省理工学院，美国）
Avoiding over-pressurization in subsurface reservoirs is critical for applications like CO2 sequestration and wastewater injection. Managing the pressures by controlling injection/extraction are challenging because of complex heterogeneity in the subsurface. The heterogeneity typically requires high-fidelity physics-based models to make predictions on CO$_2$ fate. Furthermore, characterizing the heterogeneity accurately is fraught with parametric uncertainty. Accounting for both, heterogeneity and uncertainty, makes this a computationally-intensive problem challenging for current reservoir sim…
Physics-Constrained Denoising Autoencoders for Data-Scarce Wildfire UAV Sensing
FEWALab and Geological Sciences and Geological Engineering, Queen’s University（FEWALab及地质科学与地质工程、女王大学）；School of Environmental Studies（环境研究学院；地质科学与地质工程、女王大学）；Geological Sciences and Geological Engineering, Queen’s University
University of Southern California（南加州大学）；Collaboratory for Advanced Computing and Simulations（高级计算与模拟协作实验室）
We have developed a novel differential equation solver software called PND based on the physics-informed neural network for molecular dynamics simulators. Based on automatic differentiation technique provided by Pytorch, our software allows users to flexibly implement equation of atom motions, initial and boundary conditions, and conservation laws as loss function to train the network. PND comes with a parallel molecular dynamics (MD) engine in order for users to examine and optimize loss function design, and different conservation laws and boundary conditions, and hyperparameters, thereby acc…
Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction
School of Electrical and Computer Engineering, University of Oklahoma（俄克拉荷马大学电气与计算机工程学院）；Center for Quantum Research and Technology, University of Oklahoma（俄克拉荷马大学量子研究与技术中心）；Intelligent Neuromorphic and Quantum Understanding for Innovative Research and Engineering (INQUIRE) Laboratory（创新研究与工程智能神经形态与量子理解实验室）；Material Science and Engineering Program, University of Oklahoma, Norman, OK 73019 USA（俄克拉荷马大学材料科学与工程项目，Norman, OK 73019 USA）
A Novel A.I Enhanced Reservoir Characterization with a Combined Mixture of Experts -- NVIDIA Modulus based Physics Informed Neural Operator Forward Model
We present a deep learning framework for quantifying and propagating uncertainty in systems governed by non-linear differential equations using physics-informed neural networks. Specifically, we employ latent variable models to construct probabilistic representations for the system states, and put forth an adversarial inference procedure for training them on data, while constraining their predictions to satisfy given physical laws expressed by partial differential equations. Such physics-informed constraints provide a regularization mechanism for effectively training deep generative models as …
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）
Numerous polluted groundwater sites across the globe require an active remediation strategy to restore natural environmental conditions and local ecosystem. The Engineered Nano-particles (ENPs) have emerged as an efficient reactive agent for the in-situ degradation of groundwater contaminants. While the performance of these ENPs has been highly promising on the laboratory scale, their application in real field case conditions is still limited. The complex transport and retention mechanisms of ENPs hinder the development of an efficient remediation strategy. Therefore, a predictive tool for und…
Reservoir History Matching of the Norne field with generative exotic priors and a coupled Mixture of Experts -- Physics Informed Neural Operator Forward Model
University of Pennsylvania（宾夕法尼亚大学）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）
Advances in computational science offer a principled pipeline for predictive modeling of cardiovascular flows and aspire to provide a valuable tool for monitoring, diagnostics and surgical planning. Such models can be nowadays deployed on large patient-specific topologies of systemic arterial networks and return detailed predictions on flow patterns, wall shear stresses, and pulse wave propagation. However, their success heavily relies on tedious pre-processing and calibration procedures that typically induce a significant computational cost, thus hampering their clinical applicability. In thi…
Seismic inversion using hybrid quantum neural networks
Stanford University（斯坦福大学）；Indian Institute of Technology (Indian School of Mines) Dhanbad（印度理工学院（印度矿业学院）丹巴德分校）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
