Physical modeling, simulation, and the overlap with machine learning.

Built a Modelica stock-and-flow model of reader acquisition — tracking seven stocks, six feedback loops, and a three-month simulation. The model showed the warm pool of aware-but-unconverted readers was 20× the buyer count. That changed what I did next.

Three workflows using simulation as the training environment: a neural state predictor for EV battery temperature, a CTESN surrogate model for fast optimization, and a Q-learning agent trained entirely inside simulation. All grounded in physics, all traceable to equations.

Thermal model of a house in Linköping using 10 years of PVGIS climate data. Full energy demand and PV production simulation. Payback period: 16 years in Sweden, 6 in Delhi, 3.6 in Calama, Chile. Template notebook available for any location.

Continuous-Time Echo State Networks trained on simulation data to replace expensive physics models inside calibration and optimization loops. Matches full-model dynamics, runs orders of magnitude faster, and exports as a native System Modeler component.