Traditional medical AIs are unexplainable "black boxes," while legacy mathematical simulations remain static and offline. P-DT Engine 3.0 breaks this gridlock by creating an IoT-driven, 100% explainable real-time multi-organ digital twin platform blending MIT CVSim and Guyton models.

P-DT Engine 3.0. Over the past two years, I built 100% of this system from scratch, driven by one core philosophy: We don't just code; we reconstruct the logic of life. We refuse to use "black-box AI" to blind-guess critical data. Instead, this platform couples the foundational fluid mechanics of MIT CVSim with Arthur Guyton’s multi-organ systemic equations. By solving sub-second Parameter Identification, we turn live IoT data streams into an explainable, adapting, and personalized 1:1 bio-digital replica. [Physical Body: Skin, Blood Vessels, Mucosa] ⬇️ [IoT Sensors: Multi-Parameter Real-Time Capture (PPG, BP, SpO2, ECG)] ⬇️ [P-DT Engine 3.0 Computational Core] ➔ Integrates MIT CVSim + Guyton Multi-Organ Feedback Equations ⬇️ [AI Parameter Identification] ➔ Calibrates Equation Weights in Real Time to Eliminate Individual Variance ⬇️ [High-Fidelity Physiological Feature Mapping / Right-Side Sandbox Predictive Simulation]

I built this entire physiological digital twin engine completely alone, solving fluid dynamics, boundary constraints, data discretization, non-linear overlap, numerical oscillation, and the worst memory data NaN crashes by myself. This entire core codebase was engineered under extreme resource constraints, sustained purely by a trusted friend’s personal sponsorship of approx. $300 USD per month.Maybe you will doubt whether I could actually build this. Maybe you think I am a fraud, or maybe my project is not even good enough.below this is my project

We just received the official update : Our application was not selected by Y Combinator for this batch.

CVSim-Guyton Live Solver/BioDigital-OS

IoT-Driven, Real-Time Multi-Organ Physio Digital Twin

IoT-Driven, Real-Time Multi-Organ Physio Digital Twin