1 citations · 2 across the 6 of their papers we have counts for
8 papers
The Llama 4 Herd: Architecture, Training, Evaluation, and Deployment Notes
Redacted by arXiv
This document consolidates publicly reported technical details about Metas Llama 4 model family. It summarizes (i) released variants (Scout and Maverick) and the broader herd conte…
Physics-Informed Gaussian Process Regression for the Constitutive Modeling of Concrete: A Data-Driven Improvement to Phenomenological Models
Chenyang Li, Himanshu Sharma, Youcai Wu +3
Understanding and modeling the constitutive behavior of concrete is crucial for civil and defense applications, yet widely used phenomenological models such as Karagozian \& Case c…
Physics-constrained Gaussian Processes for Predicting Shockwave Hugoniot Curves
George D. Pasparakis, Himanshu Sharma, Rushik Desai +4
A physics-constrained Gaussian Process regression framework is developed for predicting shocked material states and their associated uncertainties along the Hugoniot curve using da…
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…
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…
Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification
Himanshu Sharma, Lukáš Novák, Michael D. Shields
We present a novel physics-constrained polynomial chaos expansion as a surrogate modeling method capable of performing both scientific machine learning (SciML) and uncertainty quan…
Physics-Informed Polynomial Chaos Expansions
Lukáš Novák, Himanshu Sharma, Michael D. Shields
Surrogate modeling of costly mathematical models representing physical systems is challenging since it is typically not possible to create a large experimental design. Thus, it is…
UQpy v4.1: Uncertainty Quantification with Python
Dimitrios Tsapetis, Michael D. Shields, Dimitris G. Giovanis +9
This paper presents the latest improvements introduced in Version 4 of the UQpy, Uncertainty Quantification with Python, library. In the latest version, the code was restructured t…