In cases of Acute Circulatory Failure, fluid therapy is a commonly used intervention to stabilize heart function. However, the effectiveness of fluid therapy is not directly predictable, and the therapy can also be harmful. Physiological models can be used to predict fluid responsiveness - describing the effectiveness of fluid therapy for the patient - but require solving complex parameter identification problems. The current study aims to develop a Physics Informed Neural Network, specifically a NeuralODE-based parameter identification method for the Three Chamber cardiovascular model, which has the potential to be used to define a novel perfusion marker. The method is developed and validated on a clinical data set collected in model animal experiments.