199 citations · 302 across the 61 of their papers we have counts for
32 papers · 1 filter
GIMLET: Generalizable and Interpretable Model Learning through Embedded Thermodynamics
Suguru Shiratori, Elham Kiyani, Khemraj Shukla +1
We develop a data-driven framework for discovering constitutive relations in models of fluid flow and scalar transport. Under the assumption that velocity and/or scalar fields are…
Hybrid Iterative Solvers with Geometry-Aware Neural Preconditioners for Parametric PDEs
Youngkyu Lee, Francesc Levrero Florencio, Jay Pathak +1
The convergence behavior of classical iterative solvers for parametric partial differential equations (PDEs) is often highly sensitive to the domain and specific discretization of…
Retrofitting Earth System Models with Cadence-Limited Neural Operator Updates
Aniruddha Bora, Shixuan Zhang, Khemraj Shukla +3
Coarse resolution, imperfect parameterizations, and uncertain initial states and forcings limit Earth-system model (ESM) predictions. Traditional bias correction via data assimilat…
Kinetic-Mamba: Mamba-Assisted Predictions of Stiff Chemical Kinetics
Additi Pandey, Liang Wei, Hessam Babaee +1
Accurate chemical kinetics modeling is essential for combustion simulations, as it governs the evolution of complex reaction pathways and thermochemical states. In this work, we in…
Probabilistic Predictions of Process-Induced Deformation in Carbon/Epoxy Composites Using a Deep Operator Network
Elham Kiyani, Amit Makarand Deshpande, Madhura Limaye +7
Fiber reinforcement and polymer matrix respond differently to manufacturing conditions due to mismatch in coefficient of thermal expansion and matrix shrinkage during curing of the…
ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms
Juan Diego Toscano, Daniel T. Chen, George Em Karniadakis
Progress in computational science depends on complex numerical workflows that must faithfully encode physical laws, yet translating conceptual insight into reliable code remains a…
Adversarial Physics-Informed Machine Learning for Robust Optimal Safe Predefined-Time Stabilization: A Game-Theoretic Approach
Nick-Marios T. Kokolakis, Shanqing Liu, Jerome Darbon +2
We develop a game-theoretic framework for adversarially robust optimal safe predefined-time stabilization of parameter-dependent nonlinear dynamical systems with nonquadratic cost…
H3PC: Hypersonic, High-Order, High-Performance Code with Adaptive Mesh Refinement and Real Chemistry
Ahmad Peyvan, Khemraj Shukla, George Em Karniadakis
We have developed a hypersonic high-order, high-performance code (HPC) utilizing the ``Trixi.jl" framework in order to simulate both non-reactive and chemically reactive compre…
A Neural-Operator Preconditioned Newton Method for Accelerated Nonlinear Solvers
Youngkyu Lee, Shanqing Liu, Jerome Darbon +1
We propose a novel neural preconditioned Newton (NP-Newton) method for solving parametric nonlinear systems of equations. To overcome the stagnation or instability of Newton iterat…
From LIF to QIF: Toward Differentiable Spiking Neurons for Scientific Machine Learning
Ruyin Wan, George Em Karniadakis, Panos Stinis
Spiking neural networks (SNNs) offer biologically inspired computation but remain underexplored for continuous regression tasks in scientific machine learning. In this work, we int…
Effect of Turbulence-Closure Consistency on Airfoil Identification
Zhen Zhang, George Em Karniadakis
We consider an inverse flow problem in which the airfoil shape is identified from its wake signature, namely the velocity field in the wake of a target airfoil. This is an ill-pose…
AgenticSciML: Collaborative Multi-Agent Systems for Emergent Discovery in Scientific Machine Learning
Qile Jiang, George Karniadakis
Scientific Machine Learning (SciML) integrates data-driven inference with physical modeling to solve complex problems in science and engineering. However, the design of SciML archi…
Body-Free Simulation of Three-Dimensional Turbulent Cylinder Wakes
Zhicheng Wang, Theo Käufer, Khemraj Shukla +2
We present a body-free simulation framework for three-dimensional turbulent cylinder wakes, in which the upstream cylinder is not explicitly resolved. Instead, the incompressible N…
Turbulence Closure in RANS and Flow Inference around a Cylinder using PINNs and Sparse Experimental Data
Z. Zhang, K. Shukla, Z. Wang +8
Traditional Reynolds-averaged Navier-Stokes (RANS) closures, based on the Boussinesq eddy viscosity hypothesis and calibrated on canonical flows, often yield inaccurate predictions…
Importance of localized dilatation and distensibility in identifying determinants of thoracic aortic aneurysm with neural operators
David S. Li, Somdatta Goswami, Qianying Cao +4
Thoracic aortic aneurysms (TAAs) arise from diverse mechanical and mechanobiological disruptions to the aortic wall that increase the risk of dissection or rupture. Evidence links…
A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learning
Juan Diego Toscano, Daniel T. Chen, Vivek Oommen +2
Residual-based adaptive strategies are widely used in scientific machine learning but remain largely heuristic. We introduce a unifying variational framework that formalizes these…
Learning Nonlinear Responses in PET Bottle Buckling with a Hybrid DeepONet-Transolver Framework
Varun Kumar, Jing Bi, Cyril Ngo Ngoc +2
Neural surrogates and operator networks for solving partial differential equation (PDE) problems have attracted significant research interest in recent years. However, most existin…
Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction
Vivek Oommen, Siavash Khodakarami, Aniruddha Bora +2
Neural operators are promising surrogates for dynamical systems but when trained with standard L2 losses they tend to oversmooth fine-scale turbulent structures. Here, we show that…
Safe cross-entropy-based importance sampling for rare event simulations
Zhiwei Gao, George Karniadakis
The Improved Cross-Entropy (ICE) method is a powerful tool for estimating failure probabilities in reliability analysis. Its core idea is to approximate the optimal importance-samp…
Process-Informed Forecasting of Complex Thermal Dynamics in Pharmaceutical Manufacturing
Ramona Rubini, Siavash Khodakarami, Aniruddha Bora +2
Accurate time-series forecasting for complex physical systems is the backbone of modern industrial monitoring and control, yet deep learning models often lack the physical consiste…
Leveraging Operator Learning to Accelerate Convergence of the Preconditioned Conjugate Gradient Method
Alena Kopaničáková, Youngkyu Lee, George Em Karniadakis
We propose a new deflation strategy to accelerate the convergence of the preconditioned conjugate gradient(PCG) method for solving parametric large-scale linear systems of equation…
Automatic discovery of optimal meta-solvers for time-dependent nonlinear PDEs
Youngkyu Lee, Shanqing Liu, Jerome Darbon +1
We present a general and scalable framework for the automated discovery of optimal meta-solvers for the solution of time-dependent nonlinear partial differential equations after ap…
Crack Path Prediction with Operator Learning using Discrete Particle System data Generation
Elham Kiyani, Venkatesh Ananchaperumal, Ahmad Peyvan +3
Accurately modeling crack propagation is critical for predicting failure in engineering materials and structures, where small cracks can rapidly evolve and cause catastrophic damag…
FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design
Chenxi Wu, Juan Diego Toscano, Khemraj Shukla +7
We propose FMEnets, a physics-informed machine learning framework for the design and analysis of non-ideal plug flow reactors. FMEnets integrates the fundamental governing equation…