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Found 14 papers, 2 papers with code
Variational Bayesian Optimal Experimental Design with Normalizing Flows
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.
Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models using Markov Chain Monte Carlo
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.
Uncertainty Quantification of Graph Convolution Neural Network Models of Evolving Processes
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.
Enhancing Dynamical System Modeling through Interpretable Machine Learning Augmentations: A Case Study in Cathodic Electrophoretic Deposition
We introduce a comprehensive data-driven framework aimed at enhancing the modeling of physical systems, employing inference techniques and machine learning enhancements.
Stochastic Deep Koopman Model for Quality Propagation Analysis in Multistage Manufacturing Systems
This study introduces a stochastic deep Koopman (SDK) framework to model the complex behavior of MMSs.
Variational Sequential Optimal Experimental Design using Reinforcement Learning
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.
FP-IRL: Fokker-Planck-based Inverse Reinforcement Learning -- A Physics-Constrained Approach to Markov Decision Processes
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.
Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial
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.
Shapley-based Explainable AI for Clustering Applications in Fault Diagnosis and Prognosis
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.
Fault Prognosis of Turbofan Engines: Eventual Failure Prediction and Remaining Useful Life Estimation
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.
Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning
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.
Reconstruction of the Density Power Spectrum from Quasar Spectra using Machine Learning
With this dataset, we build a series of data-driven models to predict the power spectrum of total matter density.
Compressive sensing adaptation for polynomial chaos expansions
Basis adaptation in Homogeneous Chaos spaces rely on a suitable rotation of the underlying Gaussian germ.
Sequential Bayesian optimal experimental design via approximate dynamic programming
Advantages over batch and greedy design are then demonstrated on a nonlinear source inversion problem where we seek an optimal policy for sequential sensing.
