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Divide and conquer: Learning chaotic dynamical systems with multistep penalty neural ordinary differential equations

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Personalized Algorithm Generation: A Case Study in Learning ODE Integrators

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QSPR analysis of air-to-blood distribution of volatile organic compounds

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Kernel methods for the approximation of the eigenfunctions of the Koopman operator

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Generative Stochastic Modeling of Strongly Nonlinear Flows with Non-Gaussian Statistics

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Dynamic Modeling and Optimization of Micro-Hotplate Chemical Gas Sensors

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Discrete- vs. Continuous-Time Nonlinear Signal Processing: Attractors, Transitions and Parallel Implementation Issues

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Enhancing the Performance of Spontaneously Oscillatory Chemical Reactions

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A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms

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Feedback Control of Active Matter

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Nonlinear dimensionality reduction then and now: AIMs for dissipative PDEs in the ML era

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Black and gray box learning of amplitude equations: Application to phase field systems

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A hybrid neural network model for the dynamics of the Kuramoto‐Sivashinsky equation

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Enabling global interpolation, derivative estimation and model identification from sparse multi-experiment time series data via neural ODEs

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INDUCED ONE-PARAMETER BIFURCATIONS IN IDENTIFIED NONLINEAR DYNAMICAL MODELS

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Neural learning of chaotic dynamics: the error propagation algorithm

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Continuous Deep Equilibrium Models: Training Neural ODEs Faster by Integrating Them to Infinity

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Topological characterization versus synchronization for assessing (or not) dynamical equivalence

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Initializing LSTM internal states via manifold learning

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Optimal placement of sensor and actuator for controlling low-dimensional chaotic systems based on global modeling

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Self-consistency in neural network-based NLPC analysis with applications to time-series processing

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Noninvertibility in neural networks

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Global and local reduced models for interacting, heterogeneous agents

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Characterization of a two-parameter mixed-mode electrochemical behavior regime using neural networks

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Hausdorff metric based training of kernels to learn attractors with application to 133 chaotic dynamical systems

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Random Projection Neural Networks of Best Approximation: Convergence Theory and Practical Applications

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Kernel Sum of Squares for data adapted kernel learning of dynamical systems from data: A global optimization approach

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Discrete- vs. Continuous-Time Nonlinear Signal Processing: Attractors, Transitions and Parallel Implementation Issues

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Application of neural nets to system identification and bifurcation analysis of real world experimental data

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Characterization of a two-parameter mixed-mode electrochemical behavior regime using neural networks

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Noninvertibility and resonance in discrete-time neural networks for time-series processing

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Controlling Chaos with Artificial Neural Network: Numerical Studies and Experiments

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