Jacob Helwig
YOU?
Author Swipe
View article: NeurIPS 2024 ML4CFD Competition: Results and Retrospective Analysis
NeurIPS 2024 ML4CFD Competition: Results and Retrospective Analysis Open
The integration of machine learning (ML) into the physical sciences is reshaping computational paradigms, offering the potential to accelerate demanding simulations such as computational fluid dynamics (CFD). Yet, persistent challenges in …
A Two-Phase Deep Learning Framework for Adaptive Time-Stepping in High-Speed Flow Modeling Open
We consider the problem of modeling high-speed flows using machine learning methods. While most prior studies focus on low-speed fluid flows in which uniform time-stepping is practical, flows approaching and exceeding the speed of sound ex…
View article: A Geometry-Aware Message Passing Neural Network for Modeling Aerodynamics over Airfoils
A Geometry-Aware Message Passing Neural Network for Modeling Aerodynamics over Airfoils Open
Computational modeling of aerodynamics is a key problem in aerospace engineering, often involving flows interacting with solid objects such as airfoils. Deep surrogate models have emerged as purely data-driven approaches that learn direct …
View article: Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency
Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency Open
We consider achieving equivariance in machine learning systems via frame averaging. Current frame averaging methods involve a costly sum over large frames or rely on sampling-based approaches that only yield approximate equivariance. Here,…
View article: SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations
SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations Open
We consider using deep neural networks to solve time-dependent partial differential equations (PDEs), where multi-scale processing is crucial for modeling complex, time-evolving dynamics. While the U-Net architecture with skip connections …
View article: Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems Open
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating, and enabling our understanding of natural phenomena at a …
View article: Group Equivariant Fourier Neural Operators for Partial Differential Equations
Group Equivariant Fourier Neural Operators for Partial Differential Equations Open
We consider solving partial differential equations (PDEs) with Fourier neural operators (FNOs), which operate in the frequency domain. Since the laws of physics do not depend on the coordinate system used to describe them, it is desirable …