Takens-JEPA: Scientific Inference Across Sampling Intervals

Physical processes are often recorded at different time intervals, and a model trained at one interval can become unreliable when that interval changes. We ask whether a self-supervised representation can support scientific inference when the sampling interval at deployment differs from that used for downstream training. Takens’ embedding theorem suggests an answer: histories that end at the same mo- ment describe the same underlying state even when their samples are spaced differ- ently. Takens-JEPA turns this correspondence into an auxiliary ranking objective for a joint-embedding predictive architecture, drawing together histories that share an endpoint and separating them from histories with other endpoints. It needs no new data, since the extra histories are subsampled from stored trajectories, and adds no inference cost. On Active Matter, Rayleigh-Bénard convection, and Shear Flow from The Well, we fit parameter-estimation and endpoint-state reconstruc- tion heads to frozen representations at one interval and evaluate them unchanged at coarser, partly unseen intervals. On Active Matter, Takens-JEPA roughly halves the predictive baseline’s parameter-estimation error at every shifted interval and cuts endpoint-state error by a third; on Shear Flow it lowers parameter-estimation error under shift by up to 62% at the cost of a small loss at the training interval; gains on Rayleigh-Bénard are smaller. A control that applies the predictive objec- tive at several intervals recovers part of this gain: Takens-JEPA is more accurate at the training interval and the nearest unseen interval, matches the control at the coarsest interval, and uses about one-third less training compute.