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Physics of Decision: A data-driven framework for socio-technical systems, using parametric surrogates to enable real-time "what-if" analysis

https://doi.org/10.2139/ssrn.6568964
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control
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Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems
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Collaborative intuitionistic fuzzy-AHP to evaluate simulation-based analytics for freight transport
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Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda
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Deep learning for Covid-19 forecasting: State-of-the-art review.
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A Koopman operator framework for nonlinear epidemic dynamics: Application to an SIRSD model
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Perspectives and relationships in Supply Chain Simulation: A systematic literature review
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The new science of management decision.
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Algorithmic discovery of dynamic models from infectious disease data
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Hamiltonian Systems and Transformation in Hilbert Space
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System dynamics modelling for supply chain disruptions
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Discovering dynamic patterns from infectious disease data using dynamic mode decomposition
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Parameter-Varying Koopman Operator for Nonlinear System Modeling and Control
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Tutorial: Simulation metamodeling
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Predictive Vision of Physics of Decision : Modelisation of Virus Propagation with Force Fields Paradigm
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Resist and recover: Introducing a spring theory for modeling disaster resilience
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Modern Koopman Theory for Dynamical Systems
inferredcites →https://doi.org/10.24251/hicss.2019.369openalex-snapshot
A Tentative Framework for Risk and Opportunity Detection in A Collaborative Environment Based on Data Interpretation
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Persistent physics-based crisis management framework: A case study of traffic in the Nantes city due to flood exposure
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