Zobrazeno 1 - 10
of 251
pro vyhledávání: '"Stinis P"'
The properties of constrained fluids have increasingly gained relevance for applications ranging from materials to biology. In this work, we propose a multiscale model using twin neural networks to investigate the properties of a fluid constrained be
Externí odkaz:
http://arxiv.org/abs/2408.03263
Kolmogorov-Arnold networks (KANs) have attracted attention recently as an alternative to multilayer perceptrons (MLPs) for scientific machine learning. However, KANs can be expensive to train, even for relatively small networks. Inspired by finite ba
Externí odkaz:
http://arxiv.org/abs/2406.19662
Physics-informed deep learning has emerged as a promising alternative for solving partial differential equations. However, for complex problems, training these networks can still be challenging, often resulting in unsatisfactory accuracy and efficien
Externí odkaz:
http://arxiv.org/abs/2407.01613
Closure problems are omnipresent when simulating multiscale systems, where some quantities and processes cannot be fully prescribed despite their effects on the simulation's accuracy. Recently, scientific machine learning approaches have been propose
Externí odkaz:
http://arxiv.org/abs/2403.02913
Multiscale problems are challenging for neural network-based discretizations of differential equations, such as physics-informed neural networks (PINNs). This can be (partly) attributed to the so-called spectral bias of neural networks. To improve th
Externí odkaz:
http://arxiv.org/abs/2401.07888
Aqueous organic redox flow batteries (AORFBs) have gained popularity in renewable energy storage due to their low cost, environmental friendliness and scalability. The rapid discovery of aqueous soluble organic (ASO) redox-active materials necessitat
Externí odkaz:
http://arxiv.org/abs/2312.08481
Autor:
Kim, Youngeun, Kahana, Adar, Yin, Ruokai, Li, Yuhang, Stinis, Panos, Karniadakis, George Em, Panda, Priyadarshini
Time-To-First-Spike (TTFS) coding in Spiking Neural Networks (SNNs) offers significant advantages in terms of energy efficiency, closely mimicking the behavior of biological neurons. In this work, we delve into the role of skip connections, a widely
Externí odkaz:
http://arxiv.org/abs/2312.00919
Physics-informed neural networks and operator networks have shown promise for effectively solving equations modeling physical systems. However, these networks can be difficult or impossible to train accurately for some systems of equations. We presen
Externí odkaz:
http://arxiv.org/abs/2311.06483
Autor:
Qadeer, Saad, Engel, Andrew, Howard, Amanda, Tsou, Adam, Vargas, Max, Stinis, Panos, Chiang, Tony
Despite their immense promise in performing a variety of learning tasks, a theoretical understanding of the limitations of Deep Neural Networks (DNNs) has so far eluded practitioners. This is partly due to the inability to determine the closed forms
Externí odkaz:
http://arxiv.org/abs/2310.18612
Understanding feature representation for deep neural networks (DNNs) remains an open question within the general field of explainable AI. We use principal component analysis (PCA) to study the performance of a k-nearest neighbors classifier (k-NN), n
Externí odkaz:
http://arxiv.org/abs/2309.15328