Recent advances in artificial intelligence (AI), machine learning (ML), and deep learning (DL) are rapidly transforming photothermal, photoacoustic, and diffuse wave science and technology. These data-driven approaches provide powerful tools to address long-standing challenges in inverse problems, image reconstruction, signal interpretation, and multiscale modeling, where traditional physics-based methods often face limitations in computational efficiency and robustness. This special topic aims to integrate cutting-edge research in AI/ML/DL techniques and photothermal and photoacoustic physics, as well as more generalized diffuse wave phenomena, spanning both fundamental theory and practical applications. Interesting topics include physics-based learning, hybrid modeling frameworks, data-driven discovery of transport mechanisms, and intelligent system design for sensing and imaging. By fostering cross-disciplinary collaboration between computational scientists, physicists, and engineers, this collection aims to accelerate innovation and provide new insights into complex photo-induced thermal, acoustic, ultrasound, and generalized wave-based processes across diverse materials and systems.
- Physically Informed Neural Networks (PINNs) for Heat and Wave Equations
- Deep learning for photothermal image, photoacoustic image, and diffuse wave image reconstruction
- Machine learning for inverse problems and parameter estimation
- AI-assisted signal processing and denoising/deblurring of photothermal/photoacoustic/diffuse wave data
- Data-driven modeling of heat transport and diffusive wave mechanics
- Hybrid physics and AI framework for thermal and acoustic systems
- Neural operators and surrogate models for multiscale heat transfer
- AI-based design and optimization of photothermal, photoacoustic, and diffuse wave systems
- Uncertainty quantification and interpretability in AI-driven transport analysis (thermal, acoustic, and diffuse waves)
- High-dimensional sparse data learning in photothermal, photoacoustic, and diffuse wave imaging
- AI in nondestructive evaluation, material characterization, and biomedical diagnostics
- Real-time monitoring and control using machine learning in thermal/acoustic systems
JunYan Liu (Harbin University)
Hi Jean (Laval University)
Hu Lili (Shanghai University)
Shanghai Jiaotong (Tong University)
Jun Xia (University of Buffalo)
Pengfei Zhu (Federal Institute for Materials Testing (BAM))
