Physics-ML

The solidification surrogate behind castsolid-gnn is now public under Apache-2.0, weights included. I had expected that to be an afternoon of uploading a checkpoint. It was not. The model had lived in my training setup for months, and a training checkpoint is not something a stranger can run. Here is what stood between the checkpoint and a model someone else can use. A checkpoint carries the training rig with it My internal artifacts were a PyTorch .pt file and two scikit-learn models saved with joblib. All three were written by my own code and read by my own code, so they were full of assumptions I had stopped seeing: how features were normalized, what the architecture config was, which node ordering the mesh used, which scikit-learn version wrote the file. ...

Many casting simulation codes discretise the part on a structured cubic grid. That made sense on the hardware of the 1980s, and for many parts it still works. It is a weak base for two things foundries want now: simulating the free-form shapes that 3D-printed moulds and cores make possible, and training AI that proposes casting technology. Where the staircase hurts A cubic grid turns a curved or inclined surface into a staircase. On a plane inclined at 45°, the staircase overstates the wetted area by \(\sqrt{2} \approx 1.41\), and doubly curved surfaces are worse. The surface normal also points along a grid axis everywhere instead of following the real surface. ...