Zobrazeno 1 - 10
of 255
pro vyhledávání: '"Perdikaris, Paris"'
Autor:
Wang, Sifan, Seidman, Jacob H, Sankaran, Shyam, Wang, Hanwen, Pappas, George J., Perdikaris, Paris
Operator learning is an emerging area of machine learning which aims to learn mappings between infinite dimensional function spaces. Here we uncover a connection between operator learning architectures and conditioned neural fields from computer visi
Externí odkaz:
http://arxiv.org/abs/2405.13998
Autor:
Bodnar, Cristian, Bruinsma, Wessel P., Lucic, Ana, Stanley, Megan, Brandstetter, Johannes, Garvan, Patrick, Riechert, Maik, Weyn, Jonathan, Dong, Haiyu, Vaughan, Anna, Gupta, Jayesh K., Tambiratnam, Kit, Archibald, Alex, Heider, Elizabeth, Welling, Max, Turner, Richard E., Perdikaris, Paris
Deep learning foundation models are revolutionizing many facets of science by leveraging vast amounts of data to learn general-purpose representations that can be adapted to tackle diverse downstream tasks. Foundation models hold the promise to also
Externí odkaz:
http://arxiv.org/abs/2405.13063
Operator learning is a rising field of scientific computing where inputs or outputs of a machine learning model are functions defined in infinite-dimensional spaces. In this paper, we introduce NEON (Neural Epistemic Operator Networks), an architectu
Externí odkaz:
http://arxiv.org/abs/2404.03099
While physics-informed neural networks (PINNs) have become a popular deep learning framework for tackling forward and inverse problems governed by partial differential equations (PDEs), their performance is known to degrade when larger and deeper neu
Externí odkaz:
http://arxiv.org/abs/2402.00326
Recently deep learning surrogates and neural operators have shown promise in solving partial differential equations (PDEs). However, they often require a large amount of training data and are limited to bounded domains. In this work, we present a nov
Externí odkaz:
http://arxiv.org/abs/2308.12939
Physics-informed neural networks (PINNs) have been popularized as a deep learning framework that can seamlessly synthesize observational data and partial differential equation (PDE) constraints. Their practical effectiveness however can be hampered b
Externí odkaz:
http://arxiv.org/abs/2308.08468
Autor:
Lippe, Phillip, Veeling, Bastiaan S., Perdikaris, Paris, Turner, Richard E., Brandstetter, Johannes
Time-dependent partial differential equations (PDEs) are ubiquitous in science and engineering. Recently, mostly due to the high computational cost of traditional solution techniques, deep neural network based surrogates have gained increased interes
Externí odkaz:
http://arxiv.org/abs/2308.05732
PPDONet: Deep Operator Networks for Fast Prediction of Steady-State Solutions in Disk-Planet Systems
We develop a tool, which we name Protoplanetary Disk Operator Network (PPDONet), that can predict the solution of disk-planet interactions in protoplanetary disks in real-time. We base our tool on Deep Operator Networks (DeepONets), a class of neural
Externí odkaz:
http://arxiv.org/abs/2305.11111
Data-driven approaches achieve remarkable results for the modeling of complex dynamics based on collected data. However, these models often neglect basic physical principles which determine the behavior of any real-world system. This omission is unfa
Externí odkaz:
http://arxiv.org/abs/2305.09017
Publikováno v:
ICLR 2024
While the popularity of physics-informed neural networks (PINNs) is steadily rising, to this date, PINNs have not been successful in simulating multi-scale and singular perturbation problems. In this work, we present a new training paradigm referred
Externí odkaz:
http://arxiv.org/abs/2302.13143