Combustion machine learning: Principles, progress and prospects
Progress in combustion science and engineering has led to the generation of large amounts
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …
Human-and machine-centred designs of molecules and materials for sustainability and decarbonization
Breakthroughs in molecular and materials discovery require meaningful outliers to be
identified in existing trends. As knowledge accumulates, the inherent bias of human intuition …
identified in existing trends. As knowledge accumulates, the inherent bias of human intuition …
Machine learning: new ideas and tools in environmental science and engineering
The rapid increase in both the quantity and complexity of data that are being generated daily
in the field of environmental science and engineering (ESE) demands accompanied …
in the field of environmental science and engineering (ESE) demands accompanied …
Review of machine learning for hydrodynamics, transport, and reactions in multiphase flows and reactors
Artificial intelligence (AI), machine learning (ML), and data science are leading to a
promising transformative paradigm. ML, especially deep learning and physics-informed ML …
promising transformative paradigm. ML, especially deep learning and physics-informed ML …
[HTML][HTML] Machine learning for combustion
Combustion science is an interdisciplinary study that involves nonlinear physical and
chemical phenomena in time and length scales, including complex chemical reactions and …
chemical phenomena in time and length scales, including complex chemical reactions and …
Stiff-pinn: Physics-informed neural network for stiff chemical kinetics
The recently developed physics-informed neural network (PINN) has achieved success in
many science and engineering disciplines by encoding physics laws into the loss functions …
many science and engineering disciplines by encoding physics laws into the loss functions …
Stiff neural ordinary differential equations
ABSTRACT Neural Ordinary Differential Equations (ODEs) are a promising approach to
learn dynamical models from time-series data in science and engineering applications. This …
learn dynamical models from time-series data in science and engineering applications. This …
[HTML][HTML] Improving aircraft performance using machine learning: A review
This review covers the new developments in machine learning (ML) that are impacting the
multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics …
multi-disciplinary area of aerospace engineering, including fundamental fluid dynamics …
Machine learning for sustainable development and applications of biomass and biomass-derived carbonaceous materials in water and agricultural systems: A review
Biomass-derived materials (BDM) have broad applications in water and agricultural
systems. As an emerging tool, Machine learning (ML) has been applied to BDM systems to …
systems. As an emerging tool, Machine learning (ML) has been applied to BDM systems to …
ChemNODE: A neural ordinary differential equations framework for efficient chemical kinetic solvers
Solving for detailed chemical kinetics remains one of the major bottlenecks for
computational fluid dynamics simulations of reacting flows using a finite-rate-chemistry …
computational fluid dynamics simulations of reacting flows using a finite-rate-chemistry …