Research Models & Releases·arXiv cs.LG·17h ago

Deep learning replaces simulation for soft contact force prediction

Researchers have trained a sequence-to-sequence deep learning model to replace expensive numerical simulations for predicting adhesive forces in soft viscoelastic contacts, a critical bottleneck in soft robotics and manipulation. The model handles both short and long-range adhesion across four orders of magnitude in loading rates and varied dwell times, enabling real-time force prediction where full simulation was previously impractical. This work exemplifies how neural surrogates can accelerate physics-based design loops in robotics, shifting the computational burden from runtime simulation to offline training.

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Explainer

The paper doesn't just replace simulation with a neural model; it handles four orders of magnitude variation in loading rates and dwell times within a single sequence-to-sequence architecture, suggesting the model captures generalizable physics rather than memorizing a narrow regime.

This follows the pattern established in recent papers on thermodynamics-informed neural surrogates and amorphous materials sampling (both from late July). Like the TAIR work on supercritical combustion, this embeds domain structure (viscoelastic contact mechanics) into the learning process to improve sample efficiency. Like ATLAS, it replaces a computationally bottlenecked simulation with a learned forward map. The difference: adhesive force prediction in soft robotics is an immediate bottleneck in real systems, not a materials discovery pipeline. If this generalizes across robot morphologies and contact geometries as claimed, it becomes a practical tool rather than a research proof-of-concept.

If the authors release code and the model maintains accuracy when tested on adhesive force regimes (loading rates, materials, contact geometries) not seen during training, that confirms the model learned transferable physics. If performance degrades sharply on out-of-distribution contacts, it's a fitted interpolant with limited practical scope.

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Thermodynamics-Informed Input Reparameterization for Neural Prediction of Real-Fluid Thermodynamic Properties in Supercritical Combustion · arXiv cs.LG

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Modelwire summarizes, we don’t republish. arXiv cs.LG originally reported this story as “Deep learning-based prediction of time-resolved adhesive forces in viscoelastic Hertzian contacts”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.