Keywords

modeling, open-source, optimization, simulation, solver

Abstract

A review of current trends in scientific computing reveals a broad shift to open-source and higher-level programming languages such as Python and growing career opportunities over the next decade. Open-source modeling tools accelerate innovation in equation-based and data-driven applications. Significant resources have been deployed to develop data-driven tools (PyTorch, TensorFlow, Scikit-learn) from tech companies that rely on machine learning services to meet business needs while keeping the foundational tools open. Open-source equation-based tools such as Pyomo, CasADi, Gekko, and JuMP are also gaining momentum according to user community and development pace metrics. Integration of data-driven and principles-based tools is emerging. New compute hardware, productivity software, and training resources have the potential to radically accelerate progress. However, long-term support mechanisms are still necessary to sustain the momentum and maintenance of critical foundational packages.

Original Publication Citation

LaGrande Gunnell, Bethany Nicholson, John D. Hedengren, Equation-based and data-driven modeling: Open-source software current state and future directions, Computers & Chemical Engineering, Volume 181, 2024, 108521, ISSN 0098-1354, https://doi.org/10.1016/j.compchemeng.2023.108521.

Document Type

Peer-Reviewed Article

Publication Date

2023-11-28

Publisher

Computers & Chemical Engineering

Language

English

College

Ira A. Fulton College of Engineering

Department

Chemical Engineering

University Standing at Time of Publication

Full Professor

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