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

The Swedish Air Force turned 100, so I flew the century in simulation: seven aircraft from one Modelica library, from a hands-off glider to a drone that flies its whole mission alone. Every era fixed the last one's limitation and bought a new one — the piston ran out of air at 9 000 m, the J 35-class delta reached 11 km in 7.2 minutes but burned 30% of its fuel doing it, and the battery brought back the glider's arithmetic. Simulated in System Modeler, animated in Blender.

The transformer on your street is a 140-year-old design: iron, copper and a tank of oil. I modelled a neighborhood in Modelica and threw the same short circuit at the old gear and its solid state replacement. The old breaker let 6 000 A run for a tenth of a second; the solid state transformer never passed 430 A. Simulated in System Modeler, rendered in Blender.

Got out to a ball that pitched on middle and hit off stump, so I modelled it — multibody flight, an aerodynamic side force, and a contact model for the bounce. Length turned out not to change how much it swings at all: 0.38 m every time. What length changes is how long you get to read it — 81 ms off a full ball, 324 ms off a short one.

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.