⚙️ When building predictive models for physical systems, model size directly impacts cost, deployability, and long-term viability.
- Adagos
- il y a 50 minutes
- 1 min de lecture
An oversized model often leads to:
🔹 Over-parameterization, reducing predictability and robustness
🔹 Higher computational burden, increasing training and operational costs
🔹 Loss of control, with models that are harder to validate, maintain, and trust
The outcome is not better engineering performance. It often results in models whose accuracy does not hold under real operating conditions.
In engineering, the real question is not “How complex can we make the model?” but “What is the smallest structure that is inherently accurate and usable?”
That’s exactly what we build with NeurEco: parsimonious, physically grounded models designed to stay accurate while remaining deployable, including on constrained/embedded targets.
