Modeling of turbulent combustion system requires modeling the underlying chemistry and the turbulent flow. Solving both systems simultaneously is computationally prohibitive. Instead, given the difference in scales at which the two sub-systems evolve, the two sub-systems are typically (re)solved separately. Popular approaches such as the Flamelet Generated Manifolds (FGM) use a two-step strategy where the governing reaction kinetics are pre-computed and mapped to a low-dimensional manifold, characterized by a few reaction progress variables (model reduction) and the manifold is then ``looked-up'' during the runtime to estimate the high-dimensional system state by the flow system. While existing works have focused on these two steps independently, in this work we show that joint learning of the progress variables and the look--up model, can yield more accurate results. We build on the base formulation and implementation ChemTab to include the dynamically generated Themochemical State Variables (Lower Dimensional Dynamic Source Terms). We discuss the challenges in the implementation of this deep neural network architecture and experimentally demonstrate it's superior performance.
MIRRA: Fast, High-Fidelity Molecular Generation with Chemistry-Informed Discrete Flow Matching
Courant Institute of Mathematical Sciences, New York University（纽约大学柯朗数学科学研究所）；Tandon School of Engineering, New York University（纽约大学坦登工程学院）；Department of Physics, New York University（纽约大学物理系）
Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials
Mila - Québec Artificial Intelligence Institute（米拉-魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；University of Chinese Academy of Sciences（中国科学院大学）；Carnegie Mellon University（卡内基梅隆大学）；University of Toronto（多伦多大学）；University of California, Berkeley（加州大学伯克利分校）；Massachusetts Institute of Technology（麻省理工学院）；California Institute of Technology（加州理工学院）；National Research Council Canada（加拿大国家研究委员会）；University of Ottawa（渥太华大学）；HEC Montréal（蒙特利尔高等商学院）
SPIN-ODE: Stiff Physics-Informed Neural ODE for Chemical Reaction Rate Estimation
Institute for Atmospheric and Earth Systems Research, University of Helsinki（大气与地球系统研究所，赫尔辛基大学）；Department of Computational Engineering, Lappeenranta-Lahti University of Technology LUT（计算工程系，拉佩兰塔-拉赫蒂技术大学LUT）；Atmospheric Modelling Centre-Lahti（拉赫蒂大气建模中心）
Learning simple heuristic rules for classifying materials based on chemical composition
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA（电气工程与计算机科学系，麻省理工学院，剑桥，马萨诸塞州，02139，美国）；Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA（物理系，麻省理工学院，剑桥，马萨诸塞州，02139，美国）
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
Reactivity-Informed Machine Learning for Performance Prediction and Design Space Exploration of Alkali-Activated Slag
Department of Civil and Environmental Engineering, Rice University（Rice大学土木与环境工程系）；Rice Advanced Materials Institute, Rice University（Rice大学先进材料研究所）；Ken Kennedy Institute, Rice University（Rice大学肯尼迪研究所）
Applying Physics-Informed Enhanced Super-Resolution Generative Adversarial Networks to Turbulent Premixed Combustion and Engine-like Flame Kernel Direct Numerical Simulation Data
Jülich Supercomputing Centre, Forschungszentrum Jülich GmbH（于利希研究中心于利希超级计算中心）；RWTH Aachen University（亚琛工业大学）
Neural Network Emulator for Atmospheric Chemical ODE
School of Engineering Science, Lappeenranta-Lahti University of Technology LUT（拉普兰塔-拉赫蒂理工大学工程科学学院）；Atmospheric Modelling Centre Lahti, Lahti University Campus（拉赫蒂大学校区拉赫蒂大气建模中心）；Institute for Atmospheric and Earth System Research (INAR), The University of Helsinki（赫尔辛基大学大气与地球系统研究所（INAR））
CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling
University of California, Berkeley（加州大学伯克利分校）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；University of Cambridge（剑桥大学）；University of Minnesota（明尼苏达大学）
A semi-agnostic ansatz with variable structure for quantum machine learning
Universitat Autonoma de Barcelona（巴塞罗那自治大学）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；Institute for Quantum Computing, University of Waterloo（滑铁卢大学量子计算研究所）
