72 citations · 363 across the 68 of their papers we have counts for
10 papers · 1 filter
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…