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pro vyhledávání: '"Cooley, Madison"'
Physics-informed neural networks (PINNs) are an increasingly popular class of techniques for the numerical solution of partial differential equations (PDEs), where neural networks are trained using loss functions regularized by relevant PDE terms to
Externí odkaz:
http://arxiv.org/abs/2410.03573
Interest is rising in Physics-Informed Neural Networks (PINNs) as a mesh-free alternative to traditional numerical solvers for partial differential equations (PDEs). However, PINNs often struggle to learn high-frequency and multi-scale target solutio
Externí odkaz:
http://arxiv.org/abs/2410.03496
We present polynomial-augmented neural networks (PANNs), a novel machine learning architecture that combines deep neural networks (DNNs) with a polynomial approximant. PANNs combine the strengths of DNNs (flexibility and efficiency in higher-dimensio
Externí odkaz:
http://arxiv.org/abs/2406.02336
Publikováno v:
The Twelfth International Conference on Learning Representations (ICLR 2024)
Machine learning based solvers have garnered much attention in physical simulation and scientific computing, with a prominent example, physics-informed neural networks (PINNs). However, PINNs often struggle to solve high-frequency and multi-scale PDE
Externí odkaz:
http://arxiv.org/abs/2311.04465
Publikováno v:
Proceedings of the 2021 SIAM Conference on Applied and Computational Discrete Algorithms (ACDA21)
We present a new combinatorial model for identifying regulatory modules in gene co-expression data using a decomposition into weighted cliques. To capture complex interaction effects, we generalize the previously-studied weighted edge clique partitio
Externí odkaz:
http://arxiv.org/abs/2106.00657
Autor:
Wolf, Shaya, Moss, Fiona P., Manandhar, Rasana, Cooley, Madison, Cooley, Rafer, Burrows, Andrea Carneal, Borowczak, Mike
Publikováno v:
Proceedings of the ASEE Annual Conference & Exposition; 2019, preceding p1-8, 10p
