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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…
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…
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…
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…
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…
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…
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…
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…
Inverse Design with Dynamic Mode Decomposition
Yunpeng Zhu, Liangliang Cheng, Anping Jing +4
We introduce a computationally efficient method for the automation of inverse design in science and engineering. Based on simple least-square regression, the underlying dynamic mod…