A review of physics-informed machine learning for building energy modeling
Building energy modeling (BEM) refers to computational modeling of building energy use
and indoor dynamics. As a critical component in sustainable and resilient building …
and indoor dynamics. As a critical component in sustainable and resilient building …
Ensemble learning for physics informed neural networks: A gradient boosting approach
While the popularity of physics-informed neural networks (PINNs) is steadily rising, to this
date, PINNs have not been successful in simulating multi-scale and singular perturbation …
date, PINNs have not been successful in simulating multi-scale and singular perturbation …
Predicting Diffusion Coefficients in Nafion Membranes during the Soaking Process Using a Machine Learning Approach
I Malashin, D Daibagya, V Tynchenko, A Gantimurov… - Polymers, 2024 - mdpi.com
Nafion, a versatile polymer used in electrochemistry and membrane technologies, exhibits
complex behaviors in saline environments. This study explores Nafion membrane's IR …
complex behaviors in saline environments. This study explores Nafion membrane's IR …
[HTML][HTML] A physics-informed neural network framework for multi-physics coupling microfluidic problems
Microfluidic systems have various scientific and industrial applications, providing a powerful
means to manipulate fluids and particles on a small scale. As a crucial method to underlying …
means to manipulate fluids and particles on a small scale. As a crucial method to underlying …
1-D coupled surface flow and transport equations revisited via the physics-informed neural network approach
Abstract The de Saint-Venant equation (SVE) and advection–diffusion equation (ADE) are
commonly employed to solve solute transport problems in surface water. In this work, we …
commonly employed to solve solute transport problems in surface water. In this work, we …
Bending analysis of thin plates with variable stiffness resting on elastic foundation via a two-network strategy physics-informed neural network method
LX Peng, JK Sun, YP Tao, ZM Huang - Structures, 2024 - Elsevier
A physics-informed neural network method based on a two-network strategy is introduced to
address the bending problem of thin plates with variable stiffness resting on an elastic …
address the bending problem of thin plates with variable stiffness resting on an elastic …
Precise-Physics Driven Text-to-3D Generation
Text-to-3D generation has shown great promise in generating novel 3D content based on
given text prompts. However, existing generative methods mostly focus on geometric or …
given text prompts. However, existing generative methods mostly focus on geometric or …
[HTML][HTML] Assessment of Water Hydrochemical Parameters Using Machine Learning Tools
I Malashin, V Nelyub, A Borodulin, A Gantimurov… - Sustainability, 2025 - mdpi.com
Access to clean water is a fundamental human need, yet millions of people worldwide still
lack access to safe drinking water. Traditional water quality assessments, though reliable …
lack access to safe drinking water. Traditional water quality assessments, though reliable …
Adaptive Interface-PINNs (AdaI-PINNs) for transient diffusion: Applications to forward and inverse problems in heterogeneous media
We model transient diffusion in heterogeneous materials using a novel physics-informed
neural networks framework (PINNs) termed Adaptive interface physics-informed neural …
neural networks framework (PINNs) termed Adaptive interface physics-informed neural …
Hard-Constrained Neural Networks with Universal Approximation Guarantees
Incorporating prior knowledge or specifications of input-output relationships into machine
learning models has gained significant attention, as it enhances generalization from limited …
learning models has gained significant attention, as it enhances generalization from limited …