Kushal Sinha

Scientific Reports (2020). Describes an ensemble approach that couples discrete element method (DEM) simulations with time‑series forecasting and machine learning to reduce computational cost while retaining accuracy for agitated filter dryer simulations — an applied example of combining ML with physics‑based transient models in pharmaceutical process design.

Multiple peer‑reviewed papers on topics such as red blood cell dynamics, mixing and residence time distributions, lyophilization/scale‑up modeling, and ML‑augmented simulation tools; reflects a sustained academic record bridging first‑principles simulation and data‑driven methods.