Learning Mode-Resolved Response Computations across Systems and Forcing

Predicting complete responses to external forcing supports the design and assessment of physical systems under changing loads. Generalizing across systems requires adapting to both new excitation and different dynamic scales. We present a system-conditioned framework for full-event prediction. Its Mode-Resolved Response Operator (MRRO) uses supplied modal frequencies to condition kernel coefficients and place finite bases at each mode's scale. Propagated latent features return to node coordinates for nonlinear coupling on the query's physical-time grid, with the output anchored to a computed reference. Weights are shared within each domain and trained separately across domains. Matched experiments control physical information, phase and capacity, while assessing dependence on optimization budget. On new systems and forcing, MRRO reduces acceleration error by 45.28% and 27.83% relative to F-FNO adaptations in hysteretic and torsional networks. A joint building model covers earthquakes and six applied-force programs; five paired training runs yield 14.15% lower error than common-scale kernels on 582 responses from 128 held-out families. A proposal-conditioned reaction–diffusion model and calibrated folding-wing experiments examine extensions of the kernel construction. Frozen mechanical predictors also compare candidate actions without goal-specific training. These findings show that physical-scale allocation matters for transferable response learning.