Paul Laiu Staff Mathematician Contact LAIUMP@ORNL.GOV All Publications Uncertainty-aware inverse modeling for federated scientific discovery across DOE facilities Optimization Through Multi-Fidelity Modeling... Streaming Compression of Scientific Data via Weak-SINDy Realizability-Preserving Discontinuous Galerkin Method for Spectral Two-Moment Radiation Transport in Special Relativity... DG-IMEX Method for a Two-Moment Model for Radiation Transport in the O(v=c) Limit Privacy Preserving Federated Learning for Advanced Scientific Ecosystems MATEY: multiscale adaptive foundation models for spatiotemporal physical systems Privacy Preserving Federated Learning for Advanced Scientific Ecosystems StOKeDMD: Streaming Occupation kernel dynamic mode decomposition Convergence of Weak-SINDy Surrogate Models Anderson acceleration with approximate calculations: Applications to scientific computing Frequency Oracle for Sensitive Data Monitoring TIME-DEPENDENT SOLPS-ITER SIMULATIONS OF THE TOKAMAK PLASMA BOUNDARY FOR MODEL PREDICTIVE CONTROL Data-driven, structure-preserving approximations to entropy-based moment closures for kinetic equations Time-dependent SOLPS-ITER simulations of the tokamak plasma boundary for model predictive control using SINDy * A Neural Network Approach to Predict Gibbs Free Energy of Ternary Solid Solutions Parallel Simulated Annealing with Embedded Machine Learning and Multifidelity Models for Reactor Core Design An infeasible-start framework for convex quadratic optimization, with application to constraint-reduced interior-point and other methods Exascale models of stellar explosions: Quintessential multi-physics simulation A DG-IMEX Method for Two-moment Neutrino Transport: Nonlinear Solvers for Neutrino–Matter Coupling Analysis of a New Implicit Solver for a Semiconductor Model In Situ Compression Artifact Removal in Scientific Data Using Deep Transfer Learning and Experience Replay thornado-transport: Anderson- and GPU-accelerated nonlinear solvers for neutrino-matter coupling A fast implicit solver for semiconductor models in one space dimension Artificial Intelligence Design of Nuclear Systems Empowered by Advanced Manufacturing Pagination Current page 1 Page 2 Next page ›› Last page Last » Key Links Curriculum Vitae ORCID Google Scholar Profile Organizations Computing and Computational Sciences Directorate Computer Science and Mathematics Division Mathematics in Computation Section Multiscale Methods and Dynamics Group
Research Highlight Deep Learning for Fast and Accurate Predictions of Crystallographic and Thermodynamic Properties of Multi-Component Solid Solution Alloys
Research Highlight Deep learning for fast and accurate predictions of crystallographic and thermodynamic properties of multi-component solid solution alloys
