Yifan Wang

Yifan Wang

Thermal management in a battery-electric vehicle (BEV) is a coupled, vehicle-level problem: the battery pack, the passenger cabin, the heat pump, and cabin air quality compete for shared actuation and energy, yet most studies optimise a single subsystem on proprietary models, which prevents fair, reproducible comparison. We present OpenEV-ThermoSciML, an open and reproducible benchmark that couples a battery electro-thermal-aging model, a two-node cabin model, a heat-pump/HVAC model, and a CO$_2$/ventilation model under real driving cycles (EPA) and real weather (NREL TMY3, NASA POWER), scored by a multi-objective suite spanning battery health, PMV/PPD comfort, cabin air quality, and HVAC energy. The benchmark's battery thermal core is anchored and validated on real BEV battery-management-system (BMS) data; the reduced battery (two-state) and cabin (two-node) models are validated against converged higher-fidelity references and, for the cabin, independently cross-checked against EnergyPlus 25.2.0. On top of the benchmark we develop a physics-informed scientific-machine-learning (Sci-ML) surrogate -- a nominal-physics prior plus a learned residual with conservation penalties -- that is exact on conserved quantities and dominates black-box and Koopman surrogates out-of-distribution (overall rollout RMSE 0.014 vs 1.168 and 3.991). A shielded Sci-ML model-predictive controller (MPC) delivers statistically significant, all-positive improvements over a production-like rule-based controller across six scenarios -- including a real hot-day US06 trip (energy $-15\%$, comfort RMSE $-47\%$, peak CO$_2$ $-25\%$, battery thermal-gradient $-78\%$) -- and these gains transfer to an independently exported OpenModelica 8-node co-simulation plant.

Yifan Wang

Hybrid models combine trusted physics with data-driven correction, but a physical model is rarely wrong everywhere or in the same way. The key diagnostic question is local: where does the model fail, what missing mechanism explains the failure, and is the evidence statistically real? Existing sparse-discovery and discrepancy-learning methods usually fit one global correction, which can spread a local error into clean regimes, bias trusted physical parameters, and provide no calibrated significance for selected terms. We introduce LISDD, Localized, Identifiable Sparse Discovery of Discrepancy, a framework that localizes model error to an operating regime, identifies a sparse symbolic form for the missing mechanism, and certifies the discovery with an exact finite-sample test. LISDD fits the known physics on an automatically detected clean regime, flags discrepant regions with a calibrated residual-energy statistic, selects the local missing term by exhaustive holdout over a candidate library, and confirms significance with a sample-split $F$-test. A false-discovery-rate extension handles multiple discrepant regions with different missing mechanisms. In controlled experiments, LISDD keeps physical-parameter bias at 0.002 versus 0.43 for global-discrepancy and black-box baselines, raises localization $F_1$ from 0.44 to 0.80, recovers the correct symbolic form with probability one, attains exact detection, and controls the multi-region false-discovery rate while recovering every planted mechanism. The result is a calibrated diagnostic tool for grey-box building-energy models when a fixed physical law silently breaks in one operating regime.

Yifan Wang

Physical-structure priors such as conservation laws, Hamiltonian forms, and symmetries can improve scientific machine learning when correct, but can degrade predictions when misspecified. Existing methods usually enforce a chosen structure or tune a soft penalty, without a calibrated rule for deciding whether to impose a prior, how strongly to impose it, which prior to use, or which subset of candidate laws holds. We introduce SPADE, Structure-Prior Adaptive Decision Estimation, a closed-form framework that treats this problem as shrinkage of the structure-violating block of an unconstrained estimator. SPADE uses one exact specification test and one estimand: the test decides whether the prior is supported by data, Stein-unbiased James-Stein shrinkage sets the enforcement strength with an $O(σ^2/n)$ oracle guarantee, and a gate commits to the hard prior only when the test certifies it. The same test yields consistent nested structure selection and Benjamini-Hochberg control for subset discovery in non-nested constraint families. Across a linear-subspace prior, a reservoir conservation law, and a nonlinear Hamiltonian prior on Duffing dynamics, SPADE tracks the oracle, beats a neural-network baseline, reduces correct-prior regret from $10.3\%$ to $2.6\%$, matches cross-validation with $1/71$ of the solves, selects the correct structure with $100\%$ accuracy, and recovers partial laws with controlled false relaxation.

Yuzhen Song, Yifan Wang, Guqiao Chen et al.

On-chip inductive elements are pivotal in determining both the silicon footprint and performance of millimeter-wave (mmWave) integrated circuits. However, the layout-level synthesis of these passive devices is severely challenged by highly nonlinear geometry-to-performance mappings, computationally expensive full-wave electromagnetic simulations, topology-dependent design spaces, and the inherent non-uniqueness of inverse design. To overcome these bottlenecks, we propose a hybrid forward optimization and reverse design (HFORD) method for the target-to-layout synthesis of mmWave inductive elements. Utilizing a unified core to map device-level requirements to layout-level seeds, HFORD structures direct device targets and translates circuit specifications into a hierarchical synthesis flow. Specifically, sparse-fitting sampling is introduced to improve coverage across critical performance regions, while compact response-fitting coefficients significantly reduce training dimensionality. The HFORD core integrates a random forest for topology selection, a variational autoencoder for spectral feature generation, a mixture density network for probabilistic inverse mapping, and particle swarm optimization for latent space exploration. This integration improves the feasibility of the generated layout seeds under design rule check (DRC) constraints. Two design examples demonstrate that the proposed method accelerates the design cycle from hours to minutes compared to conventional optimization methods.