267 citations · 343 across the 11 of their papers we have counts for
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Self-explainable Operator Learning for Discovering Spatial Patterns in Functional Data
Mojgan Alishiri, Amirhossein Arzani
Operator learning has emerged as a powerful tool for modeling complex physical systems in functional spaces. However, their neural network-based architectures make them opaque mode…
SLE-FNO: Single-Layer Extensions for Task-Agnostic Continual Learning in Fourier Neural Operators
Mahmoud Elhadidy, Roshan M. D'Souza, Amirhossein Arzani
Scientific machine learning is increasingly used to build surrogate models, yet most models are trained under a restrictive assumption in which future data follow the same distribu…
Symbolic--KAN: Kolmogorov-Arnold Networks with Discrete Symbolic Structure for Interpretable Learning
Salah A Faroughi, Farinaz Mostajeran, Amirhossein Arzani +1
Symbolic discovery of governing equations is a long-standing goal in scientific machine learning, yet a fundamental trade-off persists between interpretability and scalable learnin…
Differentiable Autoencoding Neural Operator for Interpretable and Integrable Latent Space Modeling
Siva Viknesh, Amirhossein Arzani
Scientific machine learning has enabled the extraction of physical insights and data-driven modeling of high-dimensional spatiotemporal data, yet achieving physically interpretable…
ADAM-SINDy: An Efficient Optimization Framework for Parameterized Nonlinear Dynamical System Identification
Siva Viknesh, Younes Tatari, Chase Christenson +1
Identifying dynamical systems characterized by nonlinear parameters presents significant challenges in deriving mathematical models that enhance understanding of physics. Tradition…
Interpreting and generalizing deep learning in physics-based problems with functional linear models
Amirhossein Arzani, Lingxiao Yuan, Pania Newell +1
Although deep learning has achieved remarkable success in various scientific machine learning applications, its opaque nature poses concerns regarding interpretability and generali…