Constraint-Guided Symbolic Regression for Data-Efficient Kinetic Model Discovery
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866911037021749248 |
|---|---|
| author | Servia, Miguel Ángel de Carvalho Sandoval, Ilya Orson Kuok, King Hii Hellgardt, Klaus Zhang, Dongda Chanona, Ehecatl Antonio del Rio |
| author_facet | Servia, Miguel Ángel de Carvalho Sandoval, Ilya Orson Kuok, King Hii Hellgardt, Klaus Zhang, Dongda Chanona, Ehecatl Antonio del Rio |
| contents | The industrialization of catalytic processes hinges on the availability of reliable kinetic models for design, optimization, and control. Traditional mechanistic models demand extensive domain expertise, while many data-driven approaches often lack interpretability and fail to enforce physical consistency. To overcome these limitations, we propose the Physics-Informed Automated Discovery of Kinetics (PI-ADoK) framework. By integrating physical constraints directly into a symbolic regression approach, PI-ADoK narrows the search space and substantially reduces the number of experiments required for model convergence. Additionally, the framework incorporates a robust uncertainty quantification strategy via the Metropolis-Hastings algorithm, which propagates parameter uncertainty to yield credible prediction intervals. Benchmarking our method against conventional approaches across several catalytic case studies demonstrates that PI-ADoK not only enhances model fidelity but also lowers the experimental burden, highlighting its potential for efficient and reliable kinetic model discovery in chemical reaction engineering. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_02730 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Constraint-Guided Symbolic Regression for Data-Efficient Kinetic Model Discovery Servia, Miguel Ángel de Carvalho Sandoval, Ilya Orson Kuok, King Hii Hellgardt, Klaus Zhang, Dongda Chanona, Ehecatl Antonio del Rio Computational Engineering, Finance, and Science Symbolic Computation The industrialization of catalytic processes hinges on the availability of reliable kinetic models for design, optimization, and control. Traditional mechanistic models demand extensive domain expertise, while many data-driven approaches often lack interpretability and fail to enforce physical consistency. To overcome these limitations, we propose the Physics-Informed Automated Discovery of Kinetics (PI-ADoK) framework. By integrating physical constraints directly into a symbolic regression approach, PI-ADoK narrows the search space and substantially reduces the number of experiments required for model convergence. Additionally, the framework incorporates a robust uncertainty quantification strategy via the Metropolis-Hastings algorithm, which propagates parameter uncertainty to yield credible prediction intervals. Benchmarking our method against conventional approaches across several catalytic case studies demonstrates that PI-ADoK not only enhances model fidelity but also lowers the experimental burden, highlighting its potential for efficient and reliable kinetic model discovery in chemical reaction engineering. |
| title | Constraint-Guided Symbolic Regression for Data-Efficient Kinetic Model Discovery |
| topic | Computational Engineering, Finance, and Science Symbolic Computation |
| url | https://arxiv.org/abs/2507.02730 |