The past two decades have seen a major expansion in the availability, size, and precision of
time-domain data sets in astronomy. Owing to their unique combination of flexibility …

Deep learning has been shown to be an effective tool in solving partial differential equations
(PDEs) through physics-informed neural networks (PINNs). PINNs embed the PDE residual …

We consider stochastic unconstrained bilevel optimization problems when only the first-
order gradient oracles are available. While numerous optimization methods have been …

Deep learning has achieved remarkable success in diverse applications; however, its use in
solving partial differential equations (PDEs) has emerged only recently. Here, we present an …

Abstract Physics-Informed Neural Networks (PINNs) have recently attracted exponentially
increasing attention in the field of computational mechanics. This paper proposes a novel …

We introduce GWFAST, a novel Fisher-matrix code for gravitational-wave studies, tuned
toward third-generation gravitational-wave detectors such as Einstein Telescope (ET) and …

The differentiable programming paradigm is a cornerstone of modern scientific computing. It
refers to numerical methods for computing the gradient of a numerical model's output. Many …

Abstract Physics-Informed Neural Networks (PINNs) have recently gained increasing
attention in the field of topology optimization. The fusion of deep learning and topology …

The adjoint method provides an efficient way to compute sensitivities for system models with
a large number of inputs. However, implementing the adjoint method requires significant …

Uncertainty quantification serves a central role for simulation-based analysis of physical,
engineering, and biological applications using mechanistic models. From a broad …