Jenik: PDE feedback pushes physics AI models to exceed training data
Benedikt Jenik · Faster Chips That Don't Melt — Anima Anandkumar & Benedikt Jenik, Accelerated Understanding · Sep 4, 2026 · at 17:33
Benedikt Jenik describes using partial differential equations directly as dense self-improvement signals in model training.
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“You can use those PDEs both for numerical simulators to generate data, but if you're clever about it, you can even use them as a training signal. You can check how well is my model actually doing on the PDEs themselves. And use that as an additional training signal where you're suddenly in a place where you can push beyond the quality of the data in your model quality.”
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