3 citations · 9 across the 11 of their papers we have counts for
12 papers
Suêtes: An end-to-end differentiable non-hydrostatic limited-area dynamical core
Tim Whittaker, Seth Taylor, Elsa Cardoso-Bihlo +2
We present Suêtes, a fully differentiable, non-hydrostatic, limited-area atmospheric dynamical core implemented in JAX. Suêtes includes both a semi-implicit semi-Lagrangian scheme…
A solver-in-the-loop framework for end-to-end differentiable coastal hydrodynamics
Elsa Cardoso-Bihlo, Alex Bihlo
Numerical simulation of wave propagation and run-up is a cornerstone of coastal engineering and tsunami hazard assessment. However, applying these forward models to inverse problem…
Learning vertical coordinates via automatic differentiation of a dynamical core
Tim Whittaker, Seth Taylor, Elsa Cardoso-Bihlo +2
Terrain-following coordinates in atmospheric models often imprint their grid structure onto the solution, particularly over steep topography, where distorted coordinate layers can…
Diffeomorphic Neural Operator Learning
Seth Taylor, Alex Bihlo, Jean-Christophe Nave
We present an operator learning approach for a class of evolution operators using a composition of a learned lift into the space of diffeomorphisms of the domain and the group acti…
Low-rank adaptive physics-informed HyperDeepONets for solving differential equations
Etienne Zeudong, Elsa Cardoso-Bihlo, Alex Bihlo
HyperDeepONets were introduced in Lee, Cho and Hwang [ICLR, 2023] as an alternative architecture for operator learning, in which a hypernetwork generates the weights for the trunk…
ForeCite: Adapting Pre-Trained Language Models to Predict Future Citation Rates of Academic Papers
Gavin Hull, Alex Bihlo
Predicting the future citation rates of academic papers is an important step toward the automation of research evaluation and the acceleration of scientific progress. We present $\…
Mapping Galaxy Images Across Ultraviolet, Visible and Infrared Bands Using Generative Deep Learning
Youssef Zaazou, Alex Bihlo, Terrence S. Tricco
We demonstrate that generative deep learning can translate galaxy observations across ultraviolet, visible, and infrared photometric bands. Leveraging mock observations from the Il…
PinnDE: Physics-Informed Neural Networks for Solving Differential Equations
Jason Matthews, Alex Bihlo
In recent years the study of deep learning for solving differential equations has grown substantially. The use of physics-informed neural networks (PINNs) and deep operator network…
Physics-informed neural networks for tsunami inundation modeling
Rüdiger Brecht, Elsa Cardoso-Bihlo, Alex Bihlo
We use physics-informed neural networks for solving the shallow-water equations for tsunami modeling. Physics-informed neural networks are an optimization based approach for solvin…
Generalized symmetries of Burgers equation
Dmytro R. Popovych, Alex Bihlo, Roman O. Popovych
Despite the number of relevant considerations in the literature, the algebra of generalized symmetries of the Burgers equation has not been exhaustively described. We fill this gap…
Exactly conservative physics-informed neural networks and deep operator networks for dynamical systems
Elsa Cardoso-Bihlo, Alex Bihlo
We introduce a method for training exactly conservative physics-informed neural networks and physics-informed deep operator networks for dynamical systems. The method employs a pro…
Invariant Physics-Informed Neural Networks for Ordinary Differential Equations
Shivam Arora, Alex Bihlo, Francis Valiquette
Physics-informed neural networks have emerged as a prominent new method for solving differential equations. While conceptually straightforward, they often suffer training difficult…