This study presents a novel methodology for integrating physics-informed loss functions into deep learning models using OpenFOAM's comprehensive data structures. Leveraging the robust and flexible capabilities of OpenFOAM's data structure for handling complex geometries and boundary conditions, it is demonstrated how to construct detailed loss functions that accurately embed physics constraints and potentially enhance the training and performance of neural networks in handling industrial-level complicated geometry for computational fluid dynamics (CFD) simulations. The present work primarily focuses on the 1D Burger equation to showcase the detailed procedure of constructing initial loss, boundary loss, and residual loss. While the computational geometry employed here is relatively simple, the procedure is sufficiently general to illustrate its applicability to more complex computational domains. The results show the trained operator former (OFormer) neural network can successfully predict the simulation results subject to the OpenFOAM'data structure composed loss. This framework potentially opens new avenues for using deep learning to tackle complex industrial simulation challenges, promising significant advancements in the accuracy and practicality of CFD applications.
XRePIT: A deep learning-computational fluid dynamics hybrid framework implemented in OpenFOAM for fast, robust, and scalable unsteady simulations
Division of Advanced Nuclear Engineering, Pohang University of Science and Technology（浦项国立科学技术大学先进核工程系）；Division of Applied Mathematics, Brown University（布朗大学应用数学系）；Graduate School of Integrated Energy-AI, Jeonbuk National University（全北国立大学整合能源-人工智能研究生院）
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Non-Premixed Combustion on Non-Uniform Meshes and Demonstration of an Accelerated Simulation Workflow
Towards LLM-enabled autonomous combustion research: A literature-aware agent for self-corrective modeling workflows
State Key Laboratory of Turbulence and Complex Systems, School of Mechanics and Engineering Science, Peking University（湍流与复杂系统国家重点实验室，力学与工程科学学院，北京大学）；AI for Science Institute (AISI)（人工智能科学研究院（AISI））
Estimating density, velocity, and pressure fields in supersonic flow using physics-informed BOS
Pennsylvania State University（宾夕法尼亚州立大学）；CSIR-National Aerospace Laboratories（印度国家航空航天实验室）；Case Western Reserve University（凯斯西储大学）；National Research Council Canada（加拿大国家研究委员会）
acousticHelmholtzFoam: an MPI-parallel unstructured finite-volume solver for frequency-domain acoustics in heterogeneous two-phase media with perfectly matched layers
Fine-tuning a Large Language Model for Automating Computational Fluid Dynamics Simulations
State Key Laboratory for Turbulence and Complex Systems, College of Engineering, Peking University, Beijing 100871, China（湍流与复杂系统国家重点实验室，工程学院，北京大学，北京100871，中国）；HEDPS-CAPT, Peking University, Beijing 100871, China（HEDPS-CAPT，北京大学，北京100871，中国）
Anisotropic Permeability Tensor Prediction from Porous Media Microstructure via Physics-Informed Progressive Transfer Learning with Hybrid CNN-Transformer
Faculty of Mathematics and Natural Sciences, University of Oslo（奥斯陆大学数学与自然科学学院）
Physics-Informed Bayesian Learning of Electrohydrodynamic Polymer Jet Printing Dynamics
University of Patras（帕特雷大学）；Westlake University（西湖大学）；University of Santiago（圣地亚哥大学）；National Centre for Scientific Research Demokritos（国家科学研究中心“德谟克利特”）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Argonne National Laboratory（阿贡国家实验室）；Superlabs ΑΜΚΕ；Biological Lattice Industries Corp.（Biological Lattice Industries公司）
