  We introduce linear regression using physics-based basis functions optimized
through the geometry of an inner product space. This method addresses the
challenge of surrogate modeling with high-dimensional input, as the
physics-based basis functions encode problem-specific information. We
demonstrate the method using a proof-of-concept nonlinear random vibration
example.
