72 citations · 363 across the 68 of their papers we have counts for
71 papers
From Classification to Regression: Using a Fruitfly to Solve Equations
Shady E. Ahmed, Panos Stinis
We present a novel approach to regression tasks using classification which is motivated by the mechanism used by fruitflies to sense their environment. Specifically, we formulate a…
An adaptive phase field framework for large-scale interface evolution problems using a strong-form gradient smoothing approach
Zirui Mao, Alice Xu, Shenyang Hu +4
Multiscale problems with evolving interfaces are ubiquitous in science and engineering. Phase-field models are a powerful tool for simulating interface-dominated phenomena in compu…
Uncertainty-aware Multi-fidelity Closure via Conditional Normalizing Flows
Jice Zeng, Shady E. Ahmed, David Barajas-Solano +1
Reduced-order models (ROMs) provide efficient surrogates for complex multiscale systems, but their predictive accuracy is often compromised by truncation errors and the inadequate…
Mamba-Assisted Non-Markovian Closure for Reduced-Order Modeling
Zhi-Feng Wei, Saad Qadeer, Panos Stinis
Reduced-order modeling of high-dimensional dynamical systems is often hindered by closure effects arising from unresolved variables, which can introduce non-Markovian dependence in…
Assessing Emulator Design and Training for Modal Aerosol Microphysics Parameterizations in E3SMv2
Shady E. Ahmed, Hui Wan, Saad Qadeer +5
Toward the goal of using Scientific Machine Learning (SciML) emulators to improve the numerical representation of aerosol processes in global atmospheric models, we explore the emu…
Enhancing classification accuracy through chaos
Panos Stinis
We propose a novel approach which exploits chaos to enhance classification accuracy. Specifically, the available data that need to be classified are treated as vectors that are fir…
SCORE: Statistical Certification of Regions of Attraction via Extreme Value Theory
Pietro Zanotta, Panos Stinis, Ján Drgoňa
Certifying the Region of Attraction (ROA) for high-dimensional nonlinear dynamical systems remains a severe computational bottleneck. Traditional deterministic verification methods…
SINDy-KANs: Sparse identification of non-linear dynamics through Kolmogorov-Arnold networks
Amanda A. Howard, Nicholas Zolman, Bruno Jacob +2
Kolmogorov-Arnold networks (KANs) have arisen as a potential way to enhance the interpretability of machine learning. However, solutions learned by KANs are not necessarily interpr…
Improving the accuracy of physics-informed neural networks via last-layer retraining
Saad Qadeer, Panos Stinis
Physics-informed neural networks (PINNs) are a versatile tool in the burgeoning field of scientific machine learning for solving partial differential equations (PDEs). However, det…
ArGEnT: Arbitrary Geometry-encoded Transformer for Operator Learning
Wenqian Chen, Zhi-Feng Wei, Yucheng Fu +3
Learning solution operators on arbitrary geometries remains a central challenge in scientific machine learning, especially for many-query simulation, physics-informed learning, and…
Bridging quantum and classical computing for partial differential equations through multifidelity machine learning
Bruno Jacob, Amanda A. Howard, Panos Stinis
Quantum algorithms for partial differential equations (PDEs) face severe practical constraints on near-term hardware: limited qubit counts restrict spatial resolution to coarse gri…
Domain-Decomposed Graph Neural Network Surrogate Modeling for Ice Sheets
Adrienne M. Propp, Mauro Perego, Eric C. Cyr +5
Accurate yet efficient surrogate models are essential for large-scale simulations of partial differential equations (PDEs), particularly for uncertainty quantification (UQ) tasks t…
Self-adaptive weighting and sampling for physics-informed neural networks
Wenqian Chen, Amanda Howard, Panos Stinis
Physics-informed deep learning has emerged as a promising framework for solving partial differential equations (PDEs). Nevertheless, training these models on complex problems remai…
From LIF to QIF: Toward Differentiable Spiking Neurons for Scientific Machine Learning
Ruyin Wan, George Em Karniadakis, Panos Stinis
Spiking neural networks (SNNs) offer biologically inspired computation but remain underexplored for continuous regression tasks in scientific machine learning. In this work, we int…
SpikePool: Event-driven Spiking Transformer with Pooling Attention
Donghyun Lee, Alex Sima, Yuhang Li +2
Building on the success of transformers, Spiking Neural Networks (SNNs) have increasingly been integrated with transformer architectures, leading to spiking transformers that demon…
Efficient Transformer-Inspired Variants of Physics-Informed Deep Operator Networks
Zhi-Feng Wei, Wenqian Chen, Panos Stinis
Operator learning has emerged as a promising tool for accelerating the solution of partial differential equations (PDEs). The Deep Operator Networks (DeepONets) represent a pioneer…
Physics-Informed DeepONet Coupled with FEM for Convective Transport in Porous Media with Sharp Gaussian Sources
Erdi Kara, Panos Stinis
We present a hybrid framework that couples finite element methods (FEM) with physics-informed DeepONet to model fluid transport in porous media from sharp, localized Gaussian sourc…
Simulating Three-dimensional Turbulence with Physics-informed Neural Networks
Sifan Wang, Shyam Sankaran, Xiantao Fan +2
Turbulent fluid flows are among the most computationally demanding problems in science, requiring enormous computational resources that become prohibitive at high flow speeds. Phys…
Stabilizing PDE--ML coupled systems
Saad Qadeer, Panos Stinis, Hui. Wan
A long-standing obstacle in the use of machine-learnt surrogates with larger PDE systems is the onset of instabilities when solved numerically. Efforts towards ameliorating these h…
Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning
Amirhossein Mollaali, Christian Bolivar Moya, Amanda A. Howard +3
This paper explores uncertainty quantification (UQ) methods in the context of Kolmogorov-Arnold Networks (KANs). We apply an ensemble approach to KANs to obtain a heuristic measure…
E-PINNs: Epistemic Physics-Informed Neural Networks
Bruno Jacob, Ashish S. Nair, Amanda A. Howard +2
Physics-informed neural networks (PINNs) have demonstrated promise as a framework for solving forward and inverse problems involving partial differential equations. Despite recent…
What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications
Emily Williams, Amanda Howard, Brek Meuris +1
Physics-informed deep operator networks (DeepONets) have emerged as a promising approach toward numerically approximating the solution of partial differential equations (PDEs). In…
SPIKANs: Separable Physics-Informed Kolmogorov-Arnold Networks
Bruno Jacob, Amanda A. Howard, Panos Stinis
Physics-Informed Neural Networks (PINNs) have emerged as a promising method for solving partial differential equations (PDEs) in scientific computing. While PINNs typically use mul…
Multifidelity Kolmogorov-Arnold Networks
Amanda A. Howard, Bruno Jacob, Panos Stinis
We develop a method for multifidelity Kolmogorov-Arnold networks (KANs), which use a low-fidelity model along with a small amount of high-fidelity data to train a model for the hig…