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08/23/2023
Predictive Digital Twin for Optimizing Patient-Specific Radiotherapy Regimens under Uncertainty in High-Grade Gliomas
We develop a methodology to create data-driven predictive digital twins ...
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08/05/2023
Learning physics-based reduced-order models from data using nonlinear manifolds
We present a novel method for learning reduced-order models of dynamical...
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06/23/2023
Learning latent representations in high-dimensional state spaces using polynomial manifold constructions
We present a novel framework for learning cost-efficient latent represen...
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05/19/2022
Stress-constrained topology optimization of lattice-like structures using component-wise reduced order models
Lattice-like structures can provide a combination of high stiffness with...
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05/04/2022
Operator inference for non-intrusive model reduction with nonlinear manifolds
This paper proposes a novel approach for learning a data-driven quadrati...
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12/14/2021
Adaptive Projected Residual Networks for Learning Parametric Maps from Sparse Data
We present a parsimonious surrogate framework for learning high dimensio...
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01/29/2021
Reduced operator inference for nonlinear partial differential equations
We present a new scientific machine learning method that learns from dat...
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01/13/2021
Certifiable Risk-Based Engineering Design Optimization
Reliable, risk-averse design of complex engineering systems with optimiz...
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08/06/2020
Data-driven reduced-order models via regularized operator inference for a single-injector combustion process
This paper derives predictive reduced-order models for rocket engine com...
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02/22/2020
Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms
This work presents a non-intrusive model reduction method to learn low-d...
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12/17/2019
Lift Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems
We present Lift Learn, a physics-informed method for learning low-di...
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08/09/2019
Learning physics-based reduced-order models for a single-injector combustion process
This paper presents a physics-based data-driven method to learn predicti...
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05/07/2019
Multifidelity probability estimation via fusion of estimators
This paper develops a multifidelity method that enables estimation of fa...
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08/06/2018
Nonlinear Model Order Reduction via Lifting Transformations and Proper Orthogonal Decomposition
This paper presents a structure-exploiting nonlinear model reduction met...
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06/28/2018
