Adjoint Sensitivity Analysis
Adjoint sensitivity analysis is an efficient gradient computation method originating from optimal control theory. By introducing adjoint variables (Lagrange multipliers) to solve a backward system of equations, it obtains gradients of an objective function with respect to all parameters at nearly constant computational cost. The method finds application in both computational fluid dynamics and neural network training.
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The calibration step uses quasi-likelihood estimation combined with adjoint sensitivity analysis.
Adjoint sensitivity analysis, by introducing adjoint variables, requires only one additional backward equation solve to simultaneously obtain gradient information of the objective function with respect to all parameters, offering significant computational advantages over forward sensitivity analysis in high-dimensional models.
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The calibration step uses quasi-likelihood estimation combined with adjoint sensitivity analysis.
50%UnverifiedAdjoint sensitivity analysis, by introducing adjoint variables, requires only one additional backward equation solve to simultaneously obtain gradient information of the objective function with respect to all parameters, offering significant computational advantages over forward sensitivity analysis in high-dimensional models.
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