no code implementations • 8 Apr 2024 • Jiayuan Dong, Christian Jacobsen, Mehdi Khalloufi, Maryam Akram, Wanjiao Liu, Karthik Duraisamy, Xun Huan
Variational OED (vOED), in contrast, estimates a lower bound of the EIG without likelihood evaluations by approximating the posterior distributions with variational forms, and then tightens the bound by optimizing its variational parameters.
no code implementations • 26 Mar 2024 • Shijie Zhong, Wanggang Shen, Tommie Catanach, Xun Huan
We present a computational framework of predictive goal-oriented OED (GO-OED) suitable for nonlinear observation and prediction models, which seeks the experimental design providing the greatest EIG on the QoIs.
no code implementations • 17 Feb 2024 • Jeremiah Hauth, Cosmin Safta, Xun Huan, Ravi G. Patel, Reese E. Jones
In this work we present comparisons of the parametric uncertainty quantification of neural networks modeling complex spatial-temporal processes with Hamiltonian Monte Carlo and Stein variational gradient descent and its projected variant.
no code implementations • 16 Jan 2024 • Christian Jacobsen, Jiayuan Dong, Mehdi Khalloufi, Xun Huan, Karthik Duraisamy, Maryam Akram, Wanjiao Liu
We introduce a comprehensive data-driven framework aimed at enhancing the modeling of physical systems, employing inference techniques and machine learning enhancements.
no code implementations • 18 Sep 2023 • Zhiyi Chen, Harshal Maske, Huanyi Shui, Devesh Upadhyay, Michael Hopka, Joseph Cohen, Xingjian Lai, Xun Huan, Jun Ni
This study introduces a stochastic deep Koopman (SDK) framework to model the complex behavior of MMSs.
no code implementations • 17 Jun 2023 • Wanggang Shen, Jiayuan Dong, Xun Huan
We introduce variational sequential Optimal Experimental Design (vsOED), a new method for optimally designing a finite sequence of experiments under a Bayesian framework and with information-gain utilities.
no code implementations • 17 Jun 2023 • Chengyang Huang, Siddhartha Srivastava, Xun Huan, Krishna Garikipati
We identify specific manifestations of this isomorphism and use them to create a novel physics-aware IRL algorithm, FP-IRL, which can simultaneously infer the transition and reward functions using only observed trajectories.
1 code implementation • 7 May 2023 • Venkat Nemani, Luca Biggio, Xun Huan, Zhen Hu, Olga Fink, Anh Tran, Yan Wang, Xiaoge Zhang, Chao Hu
In this tutorial, we aim to provide a holistic lens on emerging UQ methods for ML models with a particular focus on neural networks and the applications of these UQ methods in tackling engineering design as well as prognostics and health management problems.
1 code implementation • 25 Mar 2023 • Joseph Cohen, Xun Huan, Jun Ni
The rules, limited to 1-2 terms utilizing original feature scales, describe 12 out of the 16 derived equipment failure clusters with precision exceeding 0. 85, showcasing the promising utility of the explainable clustering framework for intelligent manufacturing applications.
no code implementations • 23 Mar 2023 • Joseph Cohen, Xun Huan, Jun Ni
In the era of industrial big data, prognostics and health management is essential to improve the prediction of future failures to minimize inventory, maintenance, and human costs.
no code implementations • 28 Oct 2021 • Wanggang Shen, Xun Huan
We formulate this sequential optimal experimental design (sOED) problem as a finite-horizon partially observable Markov decision process (POMDP) in a Bayesian setting and with information-theoretic utilities.
no code implementations • 19 Jul 2021 • Maria Han Veiga, Xi Meng, Oleg Y. Gnedin, Nickolay Y. Gnedin, Xun Huan
With this dataset, we build a series of data-driven models to predict the power spectrum of total matter density.
no code implementations • 6 Jan 2018 • Panagiotis Tsilifis, Xun Huan, Cosmin Safta, Khachik Sargsyan, Guilhem Lacaze, Joseph C. Oefelein, Habib N. Najm, Roger G. Ghanem
Basis adaptation in Homogeneous Chaos spaces rely on a suitable rotation of the underlying Gaussian germ.
1 code implementation • 28 Apr 2016 • Xun Huan, Youssef M. Marzouk
Advantages over batch and greedy design are then demonstrated on a nonlinear source inversion problem where we seek an optimal policy for sequential sensing.