Keio University

Adaptive multiscale simulations have long faced a dilemma: when identical solvent molecules switch between resolutions, nonphysical transition forces appear, forcing a choice between distorted solvation structure and broken Hamiltonian conservation. The Integrated Molecular Model (IMM) uses a multilayer perceptron that takes only the multiscale weight functions as input to learn and cancel those transition forces, yielding artifact-free solvation structures in bulk water, aqueous K+, Na+, NH4+, Ca2+, Cl– and H3O+ systems while conserving energy over long NVE runs. The correction force costs about 1.8% of the time per step, and a model trained on K+ transfers to Na+ and NH4+ without retraining.

Adaptive multiscale simulations have long faced a dilemma: when identical solvent molecules switch between resolutions, nonphysical transition forces appear, forcing a choice between distorted solvation structure and broken Hamiltonian conservation. The Integrated Molecular Model (IMM) uses a multilayer perceptron that takes only the multiscale weight functions as input to learn and cancel those transition forces, yielding artifact-free solvation structures in bulk water, aqueous K+, Na+, NH4+, Ca2+, Cl– and H3O+ systems while conserving energy over long NVE runs. The correction force costs about 1.8% of the time per step, and a model trained on K+ transfers to Na+ and NH4+ without retraining.

Full-waveform LiDAR (FWL) records a complete return histogram for every pixel, so its data volume far exceeds a point cloud, and Transformer-based FWL models have run at only a few frames per second, short of real time. FWL-ToPM prunes low-intensity background patches by signal intensity and then merges the remaining patches along a curve that preserves spatial proximity, raising mean F1 on the Ghost-FWL benchmark from 0.503 to 0.592 while reaching 53.2 FPS. The companion FWLAug expands training samples while preserving each ray's time-of-flight waveform and the frustum geometry, adding a 4.6-point F1 gain on an out-of-distribution set of unseen scenes.

Full-waveform LiDAR (FWL) records a complete return histogram for every pixel, so its data volume far exceeds a point cloud, and Transformer-based FWL models have run at only a few frames per second, short of real time. FWL-ToPM prunes low-intensity background patches by signal intensity and then merges the remaining patches along a curve that preserves spatial proximity, raising mean F1 on the Ghost-FWL benchmark from 0.503 to 0.592 while reaching 53.2 FPS. The companion FWLAug expands training samples while preserving each ray's time-of-flight waveform and the frustum geometry, adding a 4.6-point F1 gain on an out-of-distribution set of unseen scenes.