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) |