19 citations · 19 across the 7 of their papers we have counts for
8 papers
Symbolic Neural ODEs: Learning interpretable models from time-series data
Nibodh Boddupalli, Jeff Moehlis
We present a machine learning framework for identifying sparse, interpretable models of dynamical systems directly from time-series data. Our approach parameterizes the underlying…
Symbolic Regression via Neural Networks
Nibodh Boddupalli, Timothy Matchen, Jeff Moehlis
Identifying governing equations for a dynamical system is a topic of critical interest across an array of disciplines, from mathematics to engineering to biology. Machine learning…
Expressive Symbolic Regression for Interpretable Models of Discrete-Time Dynamical Systems
Adarsh Iyer, Nibodh Boddupalli, Jeff Moehlis
Interpretable mathematical expressions defining discrete-time dynamical systems (iterated maps) can model many phenomena of scientific interest, enabling a deeper understanding of…
Extending dynamic mode decomposition to data from multiple outputs
Nibodh Boddupalli
System identification based on Koopman operator theory has grown in popularity recently. Spectral properties of the Koopman operator of a system were proven to relate to properties…
Prediction of fitness in bacteria with causal jump dynamic mode decomposition
Shara Balakrishnan, Aqib Hasnain, Nibodh Boddupalli +3
In this paper, we consider the problem of learning a predictive model for population cell growth dynamics as a function of the media conditions. We first introduce a generic data-d…
Steady state programming of controlled nonlinear systems via deep dynamic mode decomposition
Aqib Hasnain, Nibodh Boddupalli, Shara Balakrishnan +1
This paper describes the optimal selection of a control policy to program the steady state of controlled nonlinear systems with hyperbolic fixed points. This work is motivated by t…
Koopman Operators for Generalized Persistence of Excitation Conditions for Nonlinear Systems
Nibodh Boddupalli, Aqib Hasnain, Sai Pushpak Nandanoori +1
It is hard to identify nonlinear biological models strictly from data, with results that are often sensitive to experimental conditions. Automated experimental workflows and liquid…
Optimal reporter placement in sparsely measured genetic networks using the Koopman operator
Aqib Hasnain, Nibodh Boddupalli, Enoch Yeung
Optimal sensor placement is an important yet unsolved problem in control theory. In biological organisms, genetic activity is often highly nonlinear, making it difficult to design…