Paul P Laiu Staff Mathematician Contact LAIUMP@ORNL.GOV All Publications Structure-preserving neural networks for the regularized entropy-based closure of a linear, kinetic, radiative transport equa... Uncertainty-aware inverse modeling for federated scientific discovery across DOE facilities Cognitive IoT and Edge Computing for Intrusion Detection with Federated TinyML... Optimization Through Multi-Fidelity Modeling FedOSAA: Improving Federated Learning with One-Step Anderson Acceleration 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 Privacy Preserving Federated Learning for Advanced Scientific Ecosystems MATEY: multiscale adaptive foundation models for spatiotemporal physical systems 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 Time-dependent SOLPS-ITER simulations of the tokamak plasma boundary for model predictive control using SINDy * Data-driven, structure-preserving approximations to entropy-based moment closures for kinetic equations 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 Exascale models of stellar explosions: Quintessential multi-physics simulation An infeasible-start framework for convex quadratic optimization, with application to constraint-reduced interior-point and other methods 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 Pagination Current page 1 Page 2 Next page ›› Last page Last » Key Links 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
