3 papers
cs.LG2026
Uncertainty Quantification of Engineering Structures by Polynomial Chaos Expansion and Multivariate Active Learning
Qitian Lu, Jafar Jafari-Asl, Panagiotis Spyridis +1
In many engineering applications, a single high-fidelity model produces multiple quantities of interest (QoIs) under the same input parameters, e.g. finite element models of comple…
stat.ML2025
Physics-informed Polynomial Chaos Expansion with Enhanced Constrained Optimization Solver and D-optimal Sampling
Qitian Lu, Himanshu Sharma, Michael D. Shields +1
Physics-informed polynomial chaos expansions (PC) provide an efficient physically constrained surrogate modeling framework by embedding governing equations and other physical c…
stat.ML2025
Polynomial Chaos Expansion for Operator Learning
Himanshu Sharma, Lukáš Novák, Michael D. Shields
Operator learning (OL) has emerged as a powerful tool in scientific machine learning (SciML) for approximating mappings between infinite-dimensional functional spaces. One of its m…