The paper supplies explicit hand-derived gradient formulas and a full training cycle for PINNs on a simple ODE, achieving 4.29e-4 relative L2 error against the analytic solution using only the physics loss.
Almqvist , Fundamentals of physics-informed neural networks applied to solve the R eynolds boundary value problem , Lubricants, 9 (2021), p
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
math.NA 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Physics-Informed Neural Networks: A Didactic Derivation of the Complete Training Cycle
The paper supplies explicit hand-derived gradient formulas and a full training cycle for PINNs on a simple ODE, achieving 4.29e-4 relative L2 error against the analytic solution using only the physics loss.