Approaches for Creation and Evaluation of Computationally Efficient Thermofluid System Models
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Summary
The creation of candidate models through machine learning and heuristic-informed model simplification methods are explored, and the resulting candidate models are evaluated and compared through an optimal experiment design process.
- Type
- article
- Published
- 2018-01-01
- Cited by
- 0
- References
- 28
- Access
- Open access
- OpenAlex
- https://openalex.org/W2897686615
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:53514842
Keywords
Computer science, Process (computing), Heuristic, Heat exchanger, Work (physics)
References
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- The design of experiments for discriminating between two rival models
- Learning Nonlinear Dynamical Systems Using an EM Algorithm
- Optimal design : Experiments for discriminating between several models
- Learning Stable Nonlinear Dynamical Systems With Gaussian Mixture Models
- Observer Design for Stochastic Nonlinear Systems via Contraction-Based Incremental Stability
- On partial contraction analysis for coupled nonlinear oscillators
- A model reduction case study: Automotive engine air path
- Optimal design of tests for heat exchanger fouling identification
- Learning Contracting Nonlinear Dynamics From Human Demonstration for Robot Motion Planning
- Model‐based analysis of chemical‐looping combustion experiments. Part II: Optimal design of CH4‐NiO reduction experiments
- Model‐based analysis of chemical‐looping combustion experiments. Part I: Structural identifiability of kinetic models for NiO reduction
- Development, validation, and assessment of a high fidelity chilled water plant model
- Learning stable Gaussian process state space models
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