The question
A physical equation can tell us how a system should behave even when observations are scarce. Can that equation help train a neural approximation? Physics-informed neural networks combine agreement with observed data and agreement with differential-equation constraints. They can be used for forward prediction or for estimating unknown model parameters from measurements.
The idea
A neural network represents a candidate solution. Automatic differentiation computes derivatives of that candidate, and the equation turns those derivatives into a residual: a number measuring how far the equation is from being satisfied at a tested location. Training reduces a combination of residual, observation and boundary-condition errors. The choice of points and relative loss weights matters.
Why it matters
The contribution is a broadly reusable integration of differentiable models, data fitting and equation constraints. Earlier neural approaches to differential equations retain credit. Calling a model physics-informed does not make every prediction physically correct. A small average training loss can coexist with missed regions, inaccurate boundary behavior, parameter ambiguity or unstable behavior outside the tested range.
A teaching example
Consider an original classroom spring model. In a simple idealization, acceleration is proportional to minus displacement. A candidate position curve therefore implies both a displacement and a second derivative at each time. Their mismatch provides an equation residual. Checking that mismatch at a hundred selected times still does not prove it is small between those times, especially for a rapidly varying candidate curve.
Scope and a useful next step
Start with an equation whose solution is already known and keep a separate, denser checking grid. Compare solution error with training residual rather than reporting only the latter. State initial and boundary conditions and test sensitivity to sampling locations. An informative failure can show where a low residual ceases to be a reliable guide to actual solution accuracy. Two original 2017 parts bundled; not the invention of neural PDE solvers or automatic differentiation. Loss enforcement at finite collocation points does not certify exact global conservation, PDE satisfaction, identifiability, convergence or all-regime accuracy. Numerical and stochastic settings remain source-specific.
Scientific scope
Two original 2017 parts bundled; not the invention of neural PDE solvers or automatic differentiation. Loss enforcement at finite collocation points does not certify exact global conservation, PDE satisfaction, identifiability, convergence or all-regime accuracy. Numerical and stochastic settings remain source-specific.
Read the original work
- Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations
- Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations
Sources and reading locations for this explanation
- Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations
Pinned original contribution and its stated method, assumptions and limits. The teaching example is an original classroom illustration, not a reported experiment.
- Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations
Pinned original contribution and its stated method, assumptions and limits. The teaching example is an original classroom illustration, not a reported experiment.
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
Maziar Raissi; Paris Perdikaris; George Em Karniadakis
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