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Discovering stochastic dynamical equations from ecological time series data

arxiv.org · 8,431 words · saved by 1 readers

8. Department of Chemical Engineering, Indian Institute of Science, Bengaluru, Karnataka, 560012, India 9. Dept of Applied Mechanics and Biomedical Engineering, IIT Madras, Chennai, 600036, India ∗ Corresponding authors’ e-mail: arshed@iisc.ac.in, danny@iitm.ac.in and guttal@iisc.ac.in Keywords: Data Driven Model Discovery, Langevin Dynamics, Self-organization, Collective motion, Mesoscale dynamics, Data Driven Dynamical Systems, Scientific Machine Learning, Noise induced order. Theoretical studies have shown that stochasticity can affect the dynamics of ecosystems in counter-intuitive ways. However, without knowing the equations governing the dynamics of populations or ecosystems, it is difficult to ascertain the role of stochasticity in real datasets. Therefore, the inverse problem of inferring the governing stochastic equations from datasets is important. Here, we present an equation discovery methodology that takes time series data of state variables as input and outputs a stochas

Ashwin Karichannavar Shuaib Palathingal Jitesh Jhawar David B. Brückner Danny Raj M Vishwesha Guttal 1. IISc Mathematics Initiative, Indian Institute of Science, Bengaluru Karnataka, 560012, India 2. Centre for Ecological Sciences, Indian Institute of Science, Bengaluru Karnataka, 560012, India 3. University of Konstanz, Konstanz, Germany 4. Max Planck Institute of Animal Behaviour, Konstanz, Germany. 5. School of Arts and Sciences, Ahmedabad University, Ahmedabad, 380009, India. 6. Institute of Science and Technology, Austria, Am Campus 1, 3400 Klosterneuburg, Austria 7.…

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