13 citations · 27 across the 10 of their papers we have counts for
11 papers
CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Models
Stefano Riva, Carolina Introini, Antonio Cammi +13
The demand for clean energy is ever increasing, with new nuclear technologies presenting a complementary solution to renewable energies. However, designing and operating these syst…
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…
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…
PySHRED: A Python package for SHallow REcurrent Decoding for sparse sensing, model reduction and scientific discovery
David Ye, Jan Williams, Mars Gao +4
SHallow REcurrent Decoders (SHRED) provide a deep learning strategy for modeling high-dimensional dynamical systems and/or spatiotemporal data from dynamical system snapshot observ…
Reduced Order Modeling with Shallow Recurrent Decoder Networks
Matteo Tomasetto, Jan P. Williams, Francesco Braghin +2
Reduced Order Modeling is of paramount importance for efficiently inferring high-dimensional spatio-temporal fields in parametric contexts, enabling computationally tractable param…
Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks
Mars Liyao Gao, Jan P. Williams, J. Nathan Kutz
Modeling real-world spatio-temporal data is exceptionally difficult due to inherent high dimensionality, measurement noise, partial observations, and often expensive data collectio…
Reservoir computing for system identification and predictive control with limited data
Jan P. Williams, J. Nathan Kutz, Krithika Manohar
Model predictive control (MPC) is an industry standard control technique that iteratively solves an open-loop optimization problem to guide a system towards a desired state or traj…
Leveraging arbitrary mobile sensor trajectories with shallow recurrent decoder networks for full-state reconstruction
Megan R. Ebers, Jan P. Williams, Katherine M. Steele +1
Sensing is one of the most fundamental tasks for the monitoring, forecasting and control of complex, spatio-temporal systems. In many applications, a limited number of sensors are…
Sensing with shallow recurrent decoder networks
Jan P. Williams, Olivia Zahn, J. Nathan Kutz
Sensing is a universal task in science and engineering. Downstream tasks from sensing include inferring full state estimates of a system (system identification), control decisions,…
Data-driven sensor placement with shallow decoder networks
Jan Williams, Olivia Zahn, J. Nathan Kutz
Sensor placement is an important and ubiquitous problem across the engineering and physical sciences for tasks such as reconstruction, forecasting and control. Surprisingly, there…