49 citations · 172 across the 6 of their papers we have counts for
7 papers
The Smart Buildings Challenge: Learning Foundation Models for Real-World Optimization at Scale
Judah Goldfeder, Ayush Rai, Philippe M. Wyder +11
Buildings cause nearly 40\% of carbon emissions in the US. Of total building emissions, HVAC systems are responsible for approximately 40-60\% and account for 15\% of global energy…
A new approach for determining optimal placement of PM2.5 air quality sensors: case study for the contiguous United States
Makoto M. Kelp, Samuel Lin, J. Nathan Kutz +1
Considerable financial resources are allocated for measuring ambient air pollution in the United States, yet the locations for these monitoring sites may not be optimized to captur…
Machine Learning in Heterogeneous Porous Materials
Marta D'Elia, Hang Deng, Cedric Fraces +21
The "Workshop on Machine learning in heterogeneous porous materials" brought together international scientific communities of applied mathematics, porous media, and material scienc…
Deeptime: a Python library for machine learning dynamical models from time series data
Moritz Hoffmann, Martin Scherer, Tim Hempel +8
Generation and analysis of time-series data is relevant to many quantitative fields ranging from economics to fluid mechanics. In the physical sciences, structures such as metastab…
Bilinear dynamic mode decomposition for quantum control
Andy Goldschmidt, Eurika Kaiser, Jonathan L. Dubois +2
Data-driven methods for establishing quantum optimal control (QOC) using time-dependent control pulses tailored to specific quantum dynamical systems and desired control objectives…
Hierarchical Deep Learning of Multiscale Differential Equation Time-Steppers
Yuying Liu, J. Nathan Kutz, Steven L. Brunton
Nonlinear differential equations rarely admit closed-form solutions, thus requiring numerical time-stepping algorithms to approximate solutions. Further, many systems characterized…
From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction
Henning Lange, Steven L. Brunton, Nathan Kutz
We propose spectral methods for long-term forecasting of temporal signals stemming from linear and nonlinear quasi-periodic dynamical systems. For linear signals, we introduce an a…