Andrei Zakharov

Reliable estimation of electric vehicle battery capacity is critical for range prediction, maintenance planning, and second-life reuse. While vehicle manufacturers employ proprietary algorithms within the battery management system, these methods are closed, model-specific, and inaccessible to fleet operators, charging networks, and regulators. This gap creates a clear need for transferable, data-driven approaches that use universally available signals such as charging traces to deliver transparent, fleet-scale capacity estimates. We present a spectral learning framework based on Fourier Neural Operators that map per-cycle multivariate charging data to capacity estimates. The model incorporates both per-cycle predictions and sequence-level constraints to ensure smoothness and temporal consistency. Evaluated on a real-world dataset comprising 83 EVs and over 15,800 charging cycles, the method achieves a relative RMSE of 1.9%, reducing error by nearly 40% compared to strong baselines. Importantly, we show that proposed approach generalizes across vehicle manufacturers via light fine-tuning with state-of-the-art accuracy recovered on independent subsets. These results demonstrate that frequency-domain operator learning provides a compact, transferable, and interpretable tool for reliable battery health estimation beyond proprietary systems, enabling fleet-scale monitoring and supporting the wider adoption of electric mobility.

The adaptive mechanical properties of soft and fibrous biological materials are relevant to their functionality. The emergence of the macroscopic response of these materials to external stress and intrinsic cell traction from local deformations of their structural components is not well understood. Here, we investigate the nonlinear elastic behavior of blood clots by combining microscopy, rheology, and an elastic network model that incorporates the stretching, bending, and buckling of constituent fibrin fibers. By inhibiting fibrin cross-linking in blood clots, we observe an anomalous softening regime in the macroscopic shear response as well as a reduction in platelet-induced clot contractility. Our model explains these observations from two independent macroscopic measurements in a unified manner, through a single mechanical parameter, the bending stiffness of individual fibers. Supported by experimental evidence, our mechanics-based model provides a framework for predicting and comprehending the nonlinear elastic behavior of blood clots and other active biopolymer networks in general.

Significance: Crystalline sheets rolled up into cylinders occur in diverse biological and synthetic systems, including carbon nanotubes, biofilaments of the cellular cytoskeleton, and packings of colloidal particles. In this work, we show, computationally, that such tubular crystals can be programmed with reconfigurable shapes, due to motions of defects that interrupt the periodicity of the crystalline lattice. By identifying and exploiting stable patterns of these defects, we cause tubular crystals to relax into desired target geometries, a design principle that could guide the creation of versatile colloidal analogues to nanotubes. Our results suggest routes to tunable and switchable material properties in ordered, soft materials on deformable surfaces.

Morphogenetic dynamics of tissue sheets require coordinated cell shape changes regulated by global patterning of mechanical forces.

We investigate experimentally and theoretically the variety of shape transitions in structures actuated by Janus fibers. Such structures have a fast response time, well-controllable mechanics, and mimic an elementary cell of the web architecture.

We analyze the various morphing structures obtained by actuating Janus filaments comprising driven and passive sectors and textiles incorporating driven and passive filaments. Transitions between alternative shapes and coexistence of absolutely stable and metastable states within a certain range of relative extension upon actuation are detected both in Janus rings and textiles. Both single filaments and textiles can be reverse designed to bend into desired shapes by controlling both the size and orientation of driven sectors.