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Description: Reduced-order models are often obtained by projection onto a subspace; standard least squares in linear spaces is a ... Description: The advent of new powerful deep neural networks (DNNs) has fostered their application in a wide range of research ... Description: Nonlinear inverse problems and other PDE-constrained optimization problems, such as structural design under many ... Fluid phenomena are ubiquitous to our world experience: winds swooshing through trembling leaves, turbulent water streams ... Multi-scale modeling of energetic material sensitivity requires telescoping physics from the nano- and micro- and meso-scales to ...
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DDPS | AI for data-driven simulations in Physics
DDPS | 'Probabilistic methods for data-driven reduced-order modeling'
DDPS | Deep learning for reduced order modeling
DDPS | Data-driven constitutive updates: from model-free poroelasticity to level set plasticity
DDPS | Generative Machine Learning Approaches for Data-Driven Modeling and Reductions
DDPS | Modeling and controlling turbulent flows through deep learning
DDPS | Data-Driven Closure Modeling Using Derivative-free Kalman Methods
DDPS | 'Data-driven balancing transformation for predictive model order reduction'
DDPS | Cheap and robust adaptive reduced order models for nonlinear inversion and design
DDPS | Data-driven learning of nonlocal models: bridging scales and design of new neural networks
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Last Updated: June 12, 2026
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