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Modeling design and control problems involving neural network surrogates
We consider nonlinear optimization problems that involve surrogate models represented by neural networks. We demonstrate first how to directly embed...
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Reconstruction of Quasi-Local Numerical Effective Models from Low-Resolution Measurements
We consider the inverse problem of reconstructing an effective model for a prototypical diffusion process in strongly heterogeneous media based on...
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A Simulation-Extrapolation Approach to the Analysis of Interval-Censored Failure Time Data with Mis-Measured Covariates
Interval-censored failure time data arise frequently in periodical follow-up studies including clinical trials and epidemiological surveys. In...
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Towards a unified nonlocal, peridynamics framework for the coarse-graining of molecular dynamics data with fractures
Molecular dynamics (MD) has served as a powerful tool for designing materials with reduced reliance on laboratory testing. However, the use of MD...
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Optimistic NAUTILUS navigator for multiobjective optimization with costly function evaluations
We introduce novel concepts to solve multiobjective optimization problems involving (computationally) expensive function evaluations and propose a...
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Auditing and Debugging Deep Learning Models via Flip Points: Individual-Level and Group-Level Analysis
Deep learning models have been criticized for their lack of easy interpretation, which undermines confidence in their use for important applications....
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Surrogate Models for Coupled Microgrids
We consider the operation of coupled microgrids. Each microgrid consists of a number of residential energy systems, each including an energy storage...
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Intermittent Hormone Therapy Models Analysis and Bayesian Model Comparison for Prostate Cancer
The prostate is an exocrine gland of the male reproductive system dependent on androgens (testosterone and dihydrotestosterone) for development and...
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Development of an adaptive infill criterion for constrained multi-objective asynchronous surrogate-based optimization
The use of surrogate modeling techniques to efficiently solve a single objective optimization (SOO) problem has proven its worth in the optimization...
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Markov Chain Models for Cardiac Rhythm Dynamics in Patients Undergoing Catheter Ablation of Atrial Fibrillation
We have developed a novel Markov Chain modeling system that considers vectors of patients with atrial fibrillation (AF) by their AF status over a...
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A technique for non-intrusive greedy piecewise-rational model reduction of frequency response problems over wide frequency bands
In the field of model order reduction for frequency response problems, the minimal rational interpolation (MRI) method has been shown to be quite...
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Large-Scale Bayesian Optimal Experimental Design with Derivative-Informed Projected Neural Network
We address the solution of large-scale Bayesian optimal experimental design (OED) problems governed by partial differential equations (PDEs) with...
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On the maximin distance properties of orthogonal designs via the rotation
Space-filling designs are widely used in computer experiments. They are frequently evaluated by the orthogonality and distance-related criteria....
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On solving a rank regularized minimization problem via equivalent factorized column-sparse regularized models
Rank regularized minimization problem is an ideal model for the low-rank matrix completion/recovery problem. The matrix factorization approach can...
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Modeling Fluids Through Neural Networks
The process of applying neural networks to yield data-driven models for fluid simulation can be described in six steps [29]: (1) Problem formulation;...
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Optimal control of bioproduction in the presence of population heterogeneity
Cell-to-cell variability, born of stochastic chemical kinetics, persists even in large isogenic populations. In the study of single-cell dynamics...
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A mixed spectral treatment for the stochastic models with random parameters
In this paper, a mixed spectral technique is suggested for the analysis of stochastic models with parameters having random variations. The proposed...
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Surrogate-Based Ensemble Grou** Strategies for Embedded Sampling-Based Uncertainty Quantification
The embedded ensemble propagation approach introduced in Phipps et al. (SIAM J. Sci. Comput. 39(2):C162, 2017) has been demonstrated to be a powerful...
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Active Learning for Saddle Point Calculation
The saddle point (SP) calculation is a grand challenge for computationally intensive energy function in computational chemistry area, where the...
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Introductory Material to Animation and Learning
In this chapter, we introduce concepts in computer animation, starting with physics-based animation. We revise the main steps of the pipeline...