6 citations · 6 across the 4 of their papers we have counts for
4 papers
From Solvers to Research: Large Language Model-Driven Formal Mathematics at the Research Frontier
Eric Jiang, Xiao Liang, Yikai Zhang +19
Recent developments in AI for Mathematics (AI4Math), especially Large Language Model (LLM)-driven theorem provers, has achieved remarkable success in formal proof generation for we…
Regularized Reduced Order Lippman-Schwinger-Lanczos Method for Inverse Scattering Problems in the Frequency Domain
Justin Baker, Elena Cherkaev, Vladimir Druskin +2
Inverse scattering has a broad applicability in quantum mechanics, remote sensing, geophysical, and medical imaging. This paper presents a robust direct reduced order model (ROM) m…
Proximal Implicit ODE Solvers for Accelerating Learning Neural ODEs
Justin Baker, Hedi Xia, Yiwei Wang +7
Learning neural ODEs often requires solving very stiff ODE systems, primarily using explicit adaptive step size ODE solvers. These solvers are computationally expensive, requiring…
Learning POD of Complex Dynamics Using Heavy-ball Neural ODEs
Justin Baker, Elena Cherkaev, Akil Narayan +1
Proper orthogonal decomposition (POD) allows reduced-order modeling of complex dynamical systems at a substantial level, while maintaining a high degree of accuracy in modeling the…