Zobrazeno 1 - 10
of 430
pro vyhledávání: '"Willcox, Karen"'
This work develops an efficient real-time inverse formulation for inferring the aerodynamic surface pressures on a hypersonic vehicle from sparse measurements of the structural strain. The approach aims to provide real-time estimates of the aerodynam
Externí odkaz:
http://arxiv.org/abs/2408.15286
This work presents a data-driven method for learning low-dimensional time-dependent physics-based surrogate models whose predictions are endowed with uncertainty estimates. We use the operator inference approach to model reduction that poses the prob
Externí odkaz:
http://arxiv.org/abs/2408.03455
This work proposes a mathematical framework to increase the robustness to rare events of digital twins modelled with graphical models. We incorporate probabilistic model-checking and linear programming into a dynamic Bayesian network to enable the co
Externí odkaz:
http://arxiv.org/abs/2407.20490
High-performance computing (HPC) has revolutionized our ability to perform detailed simulations of complex real-world processes. A prominent contemporary example is from aerospace propulsion, where HPC is used for rotating detonation rocket engine (R
Externí odkaz:
http://arxiv.org/abs/2407.09994
This paper focuses on the construction of accurate and predictive data-driven reduced models of large-scale numerical simulations with complex dynamics and sparse training data sets. In these settings, standard, single-domain approaches may be too in
Externí odkaz:
http://arxiv.org/abs/2311.00883
This study is devoted to the construction of a multifidelity Monte Carlo (MFMC) method for the uncertainty quantification of a nonlocal, non-mass-conserving Cahn-Hilliard model for phase transitions with an obstacle potential. We are interested in th
Externí odkaz:
http://arxiv.org/abs/2310.10750
Autor:
Chaudhuri, Anirban, Pash, Graham, Hormuth II, David A., Lorenzo, Guillermo, Kapteyn, Michael, Wu, Chengyue, Lima, Ernesto A. B. F., Yankeelov, Thomas E., Willcox, Karen
Publikováno v:
Frontiers in Artificial Intelligence, 6, 2023
We develop a methodology to create data-driven predictive digital twins for optimal risk-aware clinical decision-making. We illustrate the methodology as an enabler for an anticipatory personalized treatment that accounts for uncertainties in the und
Externí odkaz:
http://arxiv.org/abs/2308.12429
We present a novel method for learning reduced-order models of dynamical systems using nonlinear manifolds. First, we learn the manifold by identifying nonlinear structure in the data through a general representation learning problem. The proposed ap
Externí odkaz:
http://arxiv.org/abs/2308.02802
The digital twin concept represents an appealing opportunity to advance condition-based and predictive maintenance paradigms for civil engineering systems, thus allowing reduced lifecycle costs, increased system safety, and increased system availabil
Externí odkaz:
http://arxiv.org/abs/2308.01445
We present a novel framework for learning cost-efficient latent representations in problems with high-dimensional state spaces through nonlinear dimension reduction. By enriching linear state approximations with low-order polynomial terms we account
Externí odkaz:
http://arxiv.org/abs/2306.13748