1 citations · 1 across the 24 of their papers we have counts for
25 papers
Learning the Koopman Operator using Attention Free Transformers
Mohammed Nagdi, Evangelos-Marios Nikolados, Alexey Yermakov +3
Learning Koopman operators with autoencoders enables linear prediction in a latent space, but long-horizon rollouts often drift off the learned manifold, leading to phase and ampli…
Neural Parameter Estimation of RC Thermal Building Models for Model Predictive Control
Fabian Raisch, Timo Germann, J. Nathan Kutz +3
Gray-box RC models are widely used to enable energy-efficient model predictive control (MPC) in buildings. However, estimating RC parameters remains difficult, as conventional opti…
Application of parametric Shallow Recurrent Decoder Network to magnetohydrodynamic flows in liquid metal blankets of fusion reactors
M. Lo Verso, C. Introini, E. Cervi +3
Magnetohydrodynamic (MHD) phenomena play a pivotal role in the design and operation of nuclear fusion systems, where electrically conducting fluids (such as liquid metals or molten…
UQ-SHRED: uncertainty quantification of shallow recurrent decoder networks for sparse sensing via engression
Mars Liyao Gao, Yuxuan Bao, Amy S. Rude +2
Reconstructing high-dimensional spatiotemporal fields from sparse sensor measurements is critical in a wide range of scientific applications. The SHallow REcurrent Decoder (SHRED)…
OpenReservoirComputing: GPU-Accelerated Reservoir Computing in JAX
Jan Williams, Dima Tretiak, Steven L. Brunton +2
OpenReservoirComputing (ORC) is a Python library for reservoir computing (RC) written in JAX (Bradbury et al. 2018) and Equinox (Kidger and Garcia 2021). JAX is a Python library fo…
Surrogate models for nuclear fusion with parametric Shallow Recurrent Decoder Networks: applications to magnetohydrodynamics
M. Lo Verso, C. Introini, E. Cervi +3
Magnetohydrodynamic (MHD) effects play a key role in the design and operation of nuclear fusion systems, where electrically conducting fluids (such as liquid metals or molten salts…
Experimental Modal Analysis for engineering structures via time-delay Dynamic Mode Decomposition with Control
Yanxin Si, Bayu Jayawardhana, J. Nathan Kutz +2
Experimental Modal Analysis (EMA) has been widely used to identify structural dynamic properties, including natural frequencies, damping ratios, and mode shapes, for structural int…
SENDAI: A Hierarchical Sparse-measurement, EfficieNt Data AssImilation Framework
Xingyue Zhang, Yuxuan Bao, Mars Liyao Gao +1
Bridging the gap between data-rich training regimes and observation-sparse deployment conditions remains a central challenge in spatiotemporal field reconstruction, particularly wh…
Cheap2Rich: A Multi-Fidelity Framework for Data Assimilation and System Identification of Multiscale Physics -- Rotating Detonation Engines
Yuxuan Bao, Jan Zajac, Megan Powers +2
Bridging the sim2real gap between computationally inexpensive models and complex physical systems remains a central challenge in machine learning applications to engineering proble…
Terrain-Adaptive Mobile 3D Printing with Hierarchical Control
Shuangshan Nors Li, J. Nathan Kutz
Mobile 3D printing on unstructured terrain remains challenging due to the conflict between platform mobility and deposition precision. Existing gantry-based systems achieve high ac…
Information theory and discriminative sampling for model discovery
Yuxuan Bao, J. Nathan Kutz
Fisher information and Shannon entropy are fundamental tools for understanding and analyzing dynamical systems from complementary perspectives. They can characterize unknown parame…
Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithms
Philippe Martin Wyder, Judah Goldfeder, Alexey Yermakov +11
Machine learning (ML) is transforming modeling and control in the physical, engineering, and biological sciences. However, rapid development has outpaced the creation of standardiz…
The Seismic Wavefield Common Task Framework
Alexey Yermakov, Yue Zhao, Marine Denolle +13
Seismology faces fundamental challenges in state forecasting and reconstruction (e.g., earthquake early warning and ground motion prediction) and managing the parametric variabilit…
A Taxonomy of Numerical Differentiation Methods
Pavel Komarov, Floris van Breugel, J. Nathan Kutz
Differentiation is a cornerstone of computing and data analysis in every discipline of science and engineering. Indeed, most fundamental physics laws are expressed as relationships…
Data assimilation and discrepancy modeling with shallow recurrent decoders
Yuxuan Bao, J. Nathan Kutz
The requirements of modern sensing are rapidly evolving, driven by increasing demands for data efficiency, real-time processing, and deployment under limited sensing coverage. Comp…
A joint optimization approach to identifying sparse dynamics using least squares kernel collocation
Alexander W. Hsu, Ike Griss Salas, Jacob M. Stevens-Haas +3
We develop an all-at-once modeling framework for learning systems of ordinary differential equations (ODE) from scarce, partial, and noisy observations of the states. The proposed…
Benchmarking Probabilistic Time Series Forecasting Models on Neural Activity
Ziyu Lu, Anna J. Li, Alexander E. Ladd +5
Neural activity forecasting is central to understanding neural systems and enabling closed-loop control. While deep learning has recently advanced the state-of-the-art in the time…
Phasor notation of Dynamic Mode Decomposition
Karl Lapo, Samuele Mosso, J. Nathan Kutz
Dynamic Mode Decomposition (DMD) is a powerful, data-driven method for diagnosing complex dynamics. Various DMD algorithms allow one to fit data with a low-rank model that decompos…
The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)
Andrew Ferguson, Marisa LaFleur, Lars Ruthotto +97
This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 w…
T-SHRED: Symbolic Regression for Regularization and Model Discovery with Transformer Shallow Recurrent Decoders
Alexey Yermakov, David Zoro, Mars Liyao Gao +1
SHallow REcurrent Decoders (SHRED) are effective for system identification and forecasting from sparse sensor measurements. Such models are light-weight and computationally efficie…
Mesh-free sparse identification of nonlinear dynamics
Mars Liyao Gao, J. Nathan Kutz, Bernat Font
Identifying the governing equations of a dynamical system is one of the most important tasks for scientific modeling. However, this procedure often requires high-quality spatio-tem…
Kernel Dynamic Mode Decomposition For Sparse Reconstruction of Closable Koopman Operators
Nishant Panda, Himanshu Singh, J. Nathan Kutz
Spatial temporal reconstruction of dynamical system is indeed a crucial problem with diverse applications ranging from climate modeling to numerous chaotic and physical processes.…
Coarse graining and reduced order models for plume ejection dynamics
Ike Griss Salas, Megan R. Ebers, Jake Stevens-Haas +1
Monitoring the atmospheric dispersion of pollutants is increasingly critical for environmental impact assessments. High-fidelity computational models are often employed to simulate…
From Models To Experiments: Shallow Recurrent Decoder Networks on the DYNASTY Experimental Facility
Stefano Riva, Andrea Missaglia, Carolina Introini +2
The Shallow Recurrent Decoder networks are a novel paradigm recently introduced for state estimation, combining sparse observations with high-dimensional model data. This architect…