Physics-informed machine learning for modeling and control of dynamical systems
Physics-informed machine learning (PIML) is a set of methods and tools that systematically
integrate machine learning (ML) algorithms with physical constraints and abstract …
integrate machine learning (ML) algorithms with physical constraints and abstract …
Statistical inference and neural network training based on stochastic difference model for air pollution and associated disease transmission
S He, M He, S Tang - Journal of Theoretical Biology, 2025 - Elsevier
A polluted air environment can potentially provoke infections of diverse respiratory diseases.
The development of mathematical models can study the mechanism of air pollution and its …
The development of mathematical models can study the mechanism of air pollution and its …
Deep-learning reconstruction of complex dynamical networks from incomplete data
Reconstructing complex networks and predicting the dynamics are particularly challenging
in real-world applications because the available information and data are incomplete. We …
in real-world applications because the available information and data are incomplete. We …
Universal Differential Equations as a Common Modeling Language for Neuroscience
A ElGazzar, M van Gerven - arXiv preprint arXiv:2403.14510, 2024 - arxiv.org
The unprecedented availability of large-scale datasets in neuroscience has spurred the
exploration of artificial deep neural networks (DNNs) both as empirical tools and as models …
exploration of artificial deep neural networks (DNNs) both as empirical tools and as models …
Swarming network inference with importance clustering of relative interactions
J Hindes, K Daley, G Stantchev… - Journal of Physics …, 2024 - iopscience.iop.org
Swarming is central to many problems in physics, biology, and engineering where collective
motion and cooperation emerge through interactions of many agents. As a result, inferring …
motion and cooperation emerge through interactions of many agents. As a result, inferring …
Observing network dynamics through sentinel nodes
A fundamental premise of statistical physics is that the particles in a physical system are
interchangeable, and hence the state of each specific component is representative of the …
interchangeable, and hence the state of each specific component is representative of the …
Data-informed modeling of the formation, persistence, and evolution of social norms and conventions
Social norms and conventions are commonly accepted and adopted behaviors and
practices within a social group that guide interactions--eg, how to spell a word or how to …
practices within a social group that guide interactions--eg, how to spell a word or how to …
Neural Differential Algebraic Equations
Differential-Algebraic Equations (DAEs) describe the temporal evolution of systems that
obey both differential and algebraic constraints. Of particular interest are systems that …
obey both differential and algebraic constraints. Of particular interest are systems that …
Data-Driven Characterization of Latent Dynamics on Quantum Testbeds
S Reddy, S Guenther, Y Cho - arXiv preprint arXiv:2401.09822, 2024 - arxiv.org
This paper presents a data-driven approach to learn latent dynamics in superconducting
quantum computing hardware. To this end, we augment the dynamical equation of quantum …
quantum computing hardware. To this end, we augment the dynamical equation of quantum …