Physics-informed machine learning methods for biomass gasification modeling by considering monotonic relationships

Shaojun Ren; Shiliang Wu; Qihang Weng

doi:10.1016/j.biortech.2022.128472

ScienceGate Book Chapters

JOURNAL ARTICLE

Physics-informed machine learning methods for biomass gasification modeling by considering monotonic relationships

Shaojun Ren Shiliang Wu Qihang Weng

Year: 2022 Journal: Bioresource Technology Vol: 369 Pages: 128472-128472 Publisher: Elsevier BV

DOI: 10.1016/j.biortech.2022.128472

Get Full-Text PDF Get Analytical Report

Keywords:

Interpretability Generalizability theory Machine learning Artificial intelligence Artificial neural network Monotonic function Experimental data Computer science Mathematics Statistics

Metrics

Cited By

4.11

FWCI (Field Weighted Citation Impact)

Refs

0.92

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Thermochemical Biomass Conversion Processes

Physical Sciences → Engineering → Biomedical Engineering

Machine Learning in Materials Science

Physical Sciences → Materials Science → Materials Chemistry

Iron and Steelmaking Processes

Physical Sciences → Engineering → Mechanical Engineering

Physics-informed machine learning methods for biomass gasification modeling by considering monotonic relationships

Metrics

Citation History

Topics

Related Documents

Predictive modeling of biomass gasification with machine learning-based regression methods

Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships

Physics-informed machine-learning for modeling aero-optics

Physics-informed machine learning for modeling multidimensional dynamics

Physics-Informed Machine Learning for DRAM Error Modeling