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Found 5 papers, 0 papers with code
Robust A-Optimal Experimental Design for Bayesian Inverse Problems
This work presents an efficient algorithmic approach for designing optimal experimental design schemes for Bayesian inverse problems such that the optimal design is robust to misspecification of elements of the inverse problem.
Coupling streaming AI and HPC ensembles to achieve 100-1000x faster biomolecular simulations
The results establish DeepDriveMD as a high-performance framework for ML-driven HPC simulation scenarios, that supports diverse MD simulation and ML back-ends, and which enables new scientific insights by improving the length and time scales accessible with current computing capacity.
The PetscSF Scalable Communication Layer
PetscSF, the communication component of the Portable, Extensible Toolkit for Scientific Computation (PETSc), is designed to provide PETSc's communication infrastructure suitable for exascale computers that utilize GPUs and other accelerators.
Distributed, Parallel, and Cluster Computing 65F10, 65F50, 68N99, 68W10 G.4; C.2
Stochastic Learning Approach to Binary Optimization for Optimal Design of Experiments
We present a novel stochastic approach to binary optimization for optimal experimental design (OED) for Bayesian inverse problems governed by mathematical models such as partial differential equations.
Training neural networks under physical constraints using a stochastic augmented Lagrangian approach
We investigate the physics-constrained training of an encoder-decoder neural network for approximating the Fokker-Planck-Landau collision operator in the 5-dimensional kinetic fusion simulation in XGC.
