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2020 – today
- 2024
[j1]Alexander Wikner
, Joseph Harvey, Michelle Girvan, Brian R. Hunt, Andrew Pomerance, Thomas M. Antonsen Jr., Edward Ott:
Stabilizing machine learning prediction of dynamics: Novel noise-inspired regularization tested with reservoir computing. Neural Networks 170: 94-110 (2024)- 2023
- [b1]Alexander Wikner:
Combining Physics-based Modeling, Machine Learning, and Data Assimilation for Forecasting Large, Complex, Spatiotemporally Chaotic Systems. University of Maryland, College Park, MD, USA, 2023 - [i5]Dhruvit Patel, Alexander Wikner:
Attention-Based Ensemble Pooling for Time Series Forecasting. CoRR abs/2310.16231 (2023) - 2022
- [i4]Alexander Wikner, Brian R. Hunt, Joseph Harvey, Michelle Girvan, Edward Ott:
Stabilizing Machine Learning Prediction of Dynamics: Noise and Noise-inspired Regularization. CoRR abs/2211.05262 (2022) - 2021
- [i3]Alexander Wikner, Jaideep Pathak, Brian R. Hunt, Istvan Szunyogh, Michelle Girvan, Edward Ott:
Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components. CoRR abs/2102.07819 (2021) - 2020
- [i2]Alexander Wikner, Jaideep Pathak, Brian R. Hunt, Michelle Girvan, Troy Arcomano, Istvan Szunyogh, Andrew Pomerance, Edward Ott:
Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems. CoRR abs/2002.05514 (2020)
2010 – 2019
- 2018
- [i1]Jaideep Pathak, Alexander Wikner, Rebeckah Fussell, Sarthak Chandra, Brian R. Hunt, Michelle Girvan, Edward Ott:
Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model. CoRR abs/1803.04779 (2018)
Coauthor Index
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