Table 1.
Type of application areas, problems, and neural networks structures of PINNs in physiological modeling.
| Area | Type | Neural networks structures |
|---|---|---|
| Cardiac electrophysiology & Hemodynamics | Forward | Feed-forward neural networks |
| Sahli et al (2020) | ||
| Xie and Yao et al (2022b) | ||
| Herrero et al (2022) | ||
| Maidu et al (2025) | ||
| Kissas et al (2020) | ||
| CNN | ||
| Jiang et al (2024) | ||
| Nazaret et al (2023) | ||
| DeepONet | ||
| Li et al (2024) | ||
| Fourier-based activation function | ||
| Aghaee and Khan (2024) | ||
| Neural network finite element model | ||
| Motiwale et al (2024) | ||
| Zhang et al (2022) | ||
| Inverse | Autoencoder | |
| Tenderini et al (2022) | ||
| Nazaret et al (2023) | ||
| Forward & Inverse | RNN | |
| Xie (2023) | ||
| Kashtanova et al (2022b) | ||
| Tenderini et al (2022) | ||
| Jiang et al (2024) | ||
|
| ||
| Neural dynamics | Forward | Transformer networks |
| Sarabian et al (2022) | ||
| Inverse | Adversarial contrastive learning | |
| Wang et al (2024) | ||
|
| ||
| Cancer | Forward | LSTM, U-Net |
| Ottens et al (2022) | ||
| Feed-forward neural networks | ||
| Mukhmetov et al (2023) | ||
|
| ||
| Electromyography | Forward | CNN |
| Li et al (2022) | ||
| Feed-forward neural networks | ||
| Taneja et al (2022) | ||
| Ma et al (2024) | ||
| Zhang et al (2022) | ||