7 papers
Physics-Informed Neural Networks for Joint Source and Parameter Estimation in Advection-Diffusion Equations
Brenda Anague, Bamdad Hosseini, Issa Karambal +1
Recent studies have demonstrated the success of deep learning in solving forward and inverse problems in engineering and scientific computing domains, such as physics-informed neur…
Conditional Sampling via Wasserstein Autoencoders and Triangular Transport
Mohammad Al-Jarrah, Michele Martino, Marcus Yim +2
We present Conditional Wasserstein Autoencoders (CWAEs), a framework for conditional simulation that exploits low-dimensional structure in both the conditioned and the conditioning…
A joint optimization approach to identifying sparse dynamics using least squares kernel collocation
Alexander W. Hsu, Ike Griss Salas, Jacob M. Stevens-Haas +3
We develop an all-at-once modeling framework for learning systems of ordinary differential equations (ODE) from scarce, partial, and noisy observations of the states. The proposed…
Operator Learning at Machine Precision
Aras Bacho, Aleksei G. Sorokin, Xianjin Yang +6
Neural operator learning methods have garnered significant attention in scientific computing for their ability to approximate infinite-dimensional operators. However, increasing th…
Learning Paths for Dynamic Measure Transport: A Control Perspective
Aimee Maurais, Bamdad Hosseini, Youssef Marzouk
We bring a control perspective to the problem of identifying paths of measures for sampling via dynamic measure transport (DMT). We highlight the fact that commonly used paths may…
Score-based deterministic density sampling
Vasily Ilin, Peter Sushko, Jingwei Hu
We propose a deterministic sampling framework using Score-Based Transport Modeling for sampling an unnormalized target density given only its score . Our metho…