arXiv subjects

Thai-Son Vu

Publications and source records attributed to Thai-Son Vu.

3 recordsLinked to original sources

arXiv2026

Reciprocity can halve what a mechanical network can learn

A passive, reciprocal, linear network driven by forces or currents and read at points it drives cannot be trained below an error floor computed from the target beforehand. The usual parameter count for such networks does not charge for Maxwell-Betti reciprocity. We prove that when such an elastic, resistor or flow network has a symmetric response operator and p driven degrees of freedom are also read, its reachable response blocks lie in a subspace of codimension p(p-1)/2, whatever its size and topology, and the floor is the distance to it. On targets with a reachable symmetric part, a second-order optimiser ends within 0.1% of the floor in 142 of 144 simulated runs, and a contrastive rule with a bond-local update direction, within its step budget, in 22 of 24. Reciprocity charges a list of single drive-read tasks only for the pairs it instruments in both directions, and a rank test on the passive network says in advance whether, and on which bonds, odd couplings locally restore the lost directions, at a cost in non-reciprocity that the target bounds from below. Under an imposed-displacement drive, the one most physical-learning hardware uses, reciprocity survives as a weaker law, an exact balance between forward and reverse transmissions once each is weighted by the driving-point compliance at its input, and an inequality on their product, under which no symmetric positive-definite chain meets both targets of a published robotic metamaterial. All nineteen published layouts we tabulated pay nothing; a force-driven layout that asks one pair to respond differently in its two directions will.

cond-mat.soft↗

arXiv2026

Blind directions of physical learning networks: where to measure and what to measure

A physical learning network is read at a few accessible nodes. Every task and learning rule that uses only the steady voltages and currents there, at a fixed operating point, acts through the boundary response map. Changes in the kernel of its Jacobian are blind to first order. A walk along one fiber ran to a 600-step cap, one edge then at 15.2 times its start. A decomposition theorem splits the response Jacobian over the hidden components. The blind dimension adds over components whose surviving slots are disjoint. A boundary-to-boundary edge adds one parameter and deletes one slot. Exposing a hidden node changes only its component. For one hidden node the contribution counts the bipartite components of the non-adjacency graph of its neighbors. For a pocket of h hidden nodes a factor-analysis bound caps what outside electrodes can expose. It is attained when every pocket node meets every neighbor and no edge joins two of those neighbors, so past a threshold further electrodes outside such a pocket expose nothing. An electrode inside such a pocket, when its hidden nodes form a clique, is worth h-1 directions where one outside is worth none. Read as vector displacements rather than potentials, the same nodes left no deficit beyond counting in all 90 spring networks tested, each a pocket fully joined to five or more accessible nodes, and nothing blind in 88. What limits the reading is the quantity measured as much as the number of contacts. Maximum-weight spanning forests give a proved upper bound on the blind dimension. The matching equality is proved for one hidden node and conjectured beyond. It holds in all 5,343 components whose maximum could be attained and certified, and the forest count matched the certified blind dimension in 1,448 of 1,500 held-out networks, where the maximum must be searched. A self-learning circuit shows the split on hardware.

physics.soc-ph↗

arXiv2010

Macroscopic behavior of bidisperse suspensions of noncolloidal particles in yield stress fluids

We study both experimentally and theoretically the rheological behavior of isotropic bidisperse suspensions of noncolloidal particles in yield stress fluids. We focus on materials in which noncolloidal particles interact with the suspending fluid only through hydrodynamical interactions. We observe that both the elastic modulus and yield stress of bidisperse suspensions are lower than those of monodisperse suspensions of same solid volume fraction. Moreover, we show that the dimensionless yield stress of such suspensions is linked to their dimensionless elastic modulus and to their solid volume fraction through the simple equation of Chateau et al.[J. rheol. 52, 489-506 (2008)]. We also show that the effect of the particle size heterogeneity can be described by means of a packing model developed to estimate random loose packing of assemblies of dry particles. All these observations finally allow us to propose simple closed form estimates for both the elastic modulus and the yield stress of bidisperse suspensions: while the elastic modulus is a function of the reduced volume fraction $ϕ/ϕ_m$ only, where $ϕ_m$ is the estimated random loose packing, the yield stress is a function of both the volume fraction $ϕ$ and the reduced volume fraction.

cond-mat.soft↗