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09/06/2023
On the Effects of Heterogeneous Errors on Multi-fidelity Bayesian Optimization
Bayesian optimization (BO) is a sequential optimization strategy that is...
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08/28/2023
Breaking Boundaries: Distributed Domain Decomposition with Scalable Physics-Informed Neural PDE Solvers
Mosaic Flow is a novel domain decomposition method designed to scale phy...
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06/15/2023
Unsupervised Anomaly Detection via Nonlinear Manifold Learning
Anomalies are samples that significantly deviate from the rest of the da...
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01/30/2023
Probabilistic Neural Data Fusion for Learning from an Arbitrary Number of Multi-fidelity Data Sets
In many applications in engineering and sciences analysts have simultane...
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11/04/2022
Multi-Fidelity Cost-Aware Bayesian Optimization
Bayesian optimization (BO) is increasingly employed in critical applicat...
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05/24/2022
Data-Driven Calibration of Multi-Fidelity Multiscale Fracture Models via Latent Map Gaussian Process
Fracture modeling of metallic alloys with microscopic pores relies on mu...
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05/24/2022
Concurrent Multiscale Damage Analysis with Adaptive Spatiotemporal Dimension Reduction
Concurrent multiscale damage models are often used to quantify the impac...
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12/04/2021
Data Fusion with Latent Map Gaussian Processes
Multi-fidelity modeling and calibration are data fusion tasks that ubiqu...
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08/08/2021
Reduced-Order Multiscale Modeling of Plastic Deformations in 3D Cast Metallic Alloys with Spatially Varying Microstructures
Cast aluminum alloys are increasingly utilized as light-weight materials...
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04/22/2021
Mosaic Flows: A Transferable Deep Learning Framework for Solving PDEs on Unseen Domains
Physics-informed neural networks (PINNs) are increasingly employed to re...
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02/07/2021
