Thrombus formation plays a crucial role in the clinical treatment of certain diseases. In conditions such as aortic dissection and cerebral aneurysm, complete thrombus occlusion in the affected region is desired to reduce blood flow into the false lumen or aneurysm sac, leading to a decrease in the tension exerted on the vascular wall and making it less likely to rupture. However, desired thrombosis sometimes fails to occur. Predicting thrombus formation can provide valuable information in such cases. This article offers a comprehensive review of conventional methods for predicting thrombus formation. In reviews conducted from the year 2000 to the present, the number of published related papers every five years has increased more than tenfold. We also found that the predictive methods can be classified into two categories: those based on the hemodynamic evaluation parameters and those based on hemodynamic and mathematical models that simulate the transport and reaction of blood components. Through our discussions, we identified several challenges that need to be resolved, including predictions based on patient-specific condition, model validation, multi-scale problems, the mechanisms of thrombus formation, and ensuring cost effectiveness. This review aims to guide researchers interested in exploring thrombus formation prediction within clinical treatments.

This study introduces a novel method called Informed Neural Networks (INNs), developed to enhance flood forecasting accuracy, particularly under limited data conditions. Accurate flood forecasts are crucial for timely evacuations, especially as heavy rainfall increasingly threatens areas previously unaffected by flooding. Traditional methods often require extensive data and frequent updates, making them costly and challenging to maintain. INNs address these challenges by enabling accurate predictions under limited data conditions. We propose an INN architecture for rivers in regions like Japan, where floods are predominantly caused by rainfall. We applied the INN to both rainfall‐dominated and non‐rainfall‐dominated floods to evaluate its effectiveness and limitations. Our experiments show that the INN effectively integrates domain knowledge, maintains performance, and achieves lower prediction errors than ANN in data‐scarce scenarios. These findings highlight the potential of INNs as a promising approach for future flood forecasting, particularly in data‐limited environments.

This study presents a novel approach to improving the accuracy of flood forecast models with limited training data. Flood forecast information is crucial for early evacuation planning. However, the probability of flooding caused by continuous heavy rainfall is increasing, even in areas where we have not installed flood forecasts. New methods exist to provide flood forecasts, but they require long-term observations and regular updating of extensive data on the basin. Existing methods of providing new flood forecast information require long-term observations and regular updates of extensive data on the watershed. These requirements are related to the construction time and cost of providing flood forecasts. To address this issue, we propose Informed Neural Networks (INN) that integrate existing domain knowledge of river engineering to enhance the performance of flood forecasts with a limited amount of training data. We evaluated the performance of our proposed method with Japanese real-world river water levels and compared it to conventional methods such as artificial neural networks (ANN). Our results demonstrate that the INN can significantly improve forecasting accuracy with only a small amount of training data, comparable to conventional methods trained with eight times the amount of flood data. This study highlights the potential of INN as a novel approach for accurate and efficient flood forecasting with limited training data.