Federated clustering under non-IID data must minimize communication rounds while preventing leakage of local data properties. This paper proposes Evolving Federated Density Peak Clustering (eFDPC), where each client incrementally learns diagonal-covariance Gaussian micro-clusters from private mini-batches, selects local density peaks, and transmits masked peaks through a shuffler-based anonymization layer. A server-side second density-peak step reconstructs unlinkable global centers from additive shares, aiming for accurate one-shot clustering without centralizing raw samples.

High-speed FMCG manufacturing loses billions to unplanned downtime because maintenance software often relies on associational predictions that can misrank corrective actions when confounders or mediators are present. This dissertation proposes PriMa-Causa, a framework that inserts a Causal Reasoning Layer between fault prediction and action recommendation, using a graph-conditioned tabular foundation model—pre-trained once on synthetic structural causal models—to answer interventional queries $P(Y \mid do(T{=}t), X{=}x)$ in a single forward pass. If the empirical chapters deliver on the design, the work could materially improve maintenance efficiency by targeting what actions do rather than what they merely predict.

This paper tackles offline parameter identification for a non-ideal DC-DC buck converter using Physics-Informed Neural Networks (PINNs). The core idea is a staged training schedule that progressively unfreezes physical parameters—inductance, capacitance, and parasitic resistances—to improve convergence and interpretability. The work matters because accurate component-level estimates enable condition monitoring and predictive maintenance without intrusive frequency-response tests.

Neural emulation need not require learning a substitute for known physics. This paper investigates PolyNets that compile continuous-time polynomial dynamics and discrete-time RK4 integration into a single tensor graph, benchmarking the approach on the 1D Korteweg–de Vries equation against classical CPU/GPU solvers and three trained neural models. The central finding is that when governing coefficients are known, the resulting network requires no training yet preserves numerical fidelity while achieving competitive GPU throughput.

Planetary landing controllers learned via deep RL are usually randomized with independent disturbances, yet real mission perturbations are temporally correlated. This paper proposes adversarial RL where a second agent generates smooth, physically bounded disturbance sequences to stress-test a 3-DOF lunar lander, framing training as an asymmetric zero-sum game. A staged curriculum (nominal, stochastic, adversarial) aims to expose hidden failure modes that standard uncorrelated randomization misses. Early results suggest the adversarially hardened policy resists larger out-of-distribution perturbations better, though at some cost in low-disturbance accuracy and with limited statistical validation.

Supermartingale certificates guarantee temporal properties of stochastic dynamical systems, but their validity depends on a design-time model that may not match the black-box deployment environment. This paper introduces a runtime monitoring framework that observes a single live trajectory and uses betting-based sequential testing to raise an alarm when statistical evidence suggests the certificate has been violated. By applying Ville’s inequality, the method provides a user-specified bound on the probability of ever raising a false alarm, closing the sim-to-real gap for probabilistic certificates.

Quadrupedal locomotion requires balancing command tracking, stability, and energy efficiency, yet conventional RL bakes these trade-offs into a static scalar reward that cannot change after training. PROMO proposes a preference-conditioned multi-objective framework that takes the relative priority vector $w_t \in \Delta^{K-1}$ with $K=3$ as a runtime input to a single policy, while keeping embodiment-specific locomotion priors fixed. On a Unitree Go2, the same policy transferred zero-shot modulates behavior through preference changes alone, cutting specific energy by up to $30.4\%$, position error by $38.7\%$, and peak body-attitude deviation by $59.0\%$.

This paper tackles deterministic multi-impulse interplanetary rendezvous by embedding classical astrodynamics inside a reinforcement-learning policy. The proposed Reachability Analysis-Informed RL (RARL) framework maps bounded velocity perturbations into a local ellipsoidal waypoint set via the first-order position-from-velocity sensitivity matrix; the policy selects a waypoint, and a Lambert solver reconstructs the dynamically consistent impulsive arc. The core contribution is a physically structured action interface that supports both near-optimal nominal transfers and policy reuse across a family of dispersed departure states.

This work tackles decentralized learning in multi-player stochastic games where each agent's state transitions depend only on its own state and action, but rewards are arbitrarily coupled. The core idea is to run independent online mirror descent in the dual space of local occupancy measures, using asynchronous dyadic confidence sets to handle unknown transition kernels without synchronized episodes. This matters because the guarantees scale with local state-action dimensions and mixing times rather than the joint product space, offering the first fully online, uncoordinated framework for this setting.

Standard stable port-Hamiltonian neural networks (s-PHNNs) enforce a single global minimum in the learned Hamiltonian, limiting them to systems with exactly one attractor. The authors overcome this by parametrizing the Hamiltonian as a product of Bregman divergences generated by a single fully input-convex neural network (FICNN), yielding a model---the ms-PHNN---that can represent multiple asymptotically stable equilibria while retaining local stability certificates by construction. This matters because many physical systems, from buckled beams to magnetic pendulums, exhibit multi-stable energy landscapes that existing guaranteed-stable neural ODEs cannot capture.

This paper introduces BOOST-RPF, which tackles power flow analysis in distribution grids by reformulating voltage prediction from a global graph regression task into a sequential path-based learning problem. The key insight is leveraging the radial (tree) topology of distribution networks to decompose them into root-to-leaf paths, then using XGBoost to predict local voltage drops between parent-child bus pairs. This approach aims to combine the speed of machine learning with the size-agnostic, recursive inductive bias of classical solvers like DistFlow.

Ctrl-A addresses automated data augmentation by framing it as a control problem, dynamically adjusting per-operation augmentation strengths via a feedback loop that balances training and validation loss ratios. The method introduces Relative Operation Response (ROR) curves to individually tune transformation distributions without manual initialization or expensive search phases. While it achieves competitive results on CIFAR and SVHN benchmarks with minimal computational overhead (~10% vs. TrivialAugment), the evaluation relies on a modified training setup with extended epochs, raising questions about separability of algorithmic gains from training protocol changes.

The paper addresses multi-UAV coordination under intermittent communications by proposing a Spatio-Temporal Attention enhanced MADRL (STA-MADRL) framework. It combines delay-penalized rewards to incentivize information exchange with a prediction module that recovers missing state data using temporal and spatial attention mechanisms. The authors claim 75% throughput improvements over communication-limited baselines while achieving near-ideal performance without requiring real-time global state sharing.