arXiv Machine Learning
Jul 7
RL-Ballast: Ship Ballast Water Path Planning and Clog Prediction via Reinforcement Learning
arXiv:2607. 04906v1 Announce Type: new Abstract: Under the Shipping 4.
A self-learning framework for adaptive free-form pipe routing in aeroengines
The paper introduces SLPR, a self‑learning framework that uses proximal policy optimization to optimize free‑form pipe routing in aeroengines. It incorporates a unified rule‑modeling system for obstacle detection, a potential energy table for rapid layout queries, and iteratively refines routing via agent‑environment interaction. Experiments show SLPR outperforms three baselines in pipe length, rule adherence, path complexity, and computational efficiency, and adapts quickly to changing design environments.
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arXiv AI
Aug 20
Hybrid Reinforcement Learning and Search for Flight Trajectory Planning
The paper investigates combining Reinforcement Learning (RL) with search-based path planners to accelerate flight trajectory optimization for airliners. An RL agent is trained to generate near‑optimal paths from location and atmospheric data, which then constrain a traditional solver to reduce its search space. Experiments using Airbus performance models show that fuel consumption deviates by less than 1% from an unconstrained solver while computation time improves by up to 50%.
arXiv Machine Learning
Jul 14
A Multi-Agent Framework for Zero-Dimensional Reduced-Order Model Planning
arXiv:2607. 10994v1 Announce Type: new Abstract: Zero-dimensional reduced-order models (0D ROMs) are central to multi-dimensional design workflows for high-end complex equipment.
arXiv AI
Oct 2
CANTO: CAD-Native Transformer Operators for AI-Aided Engineering
CANTO is a transformer neural operator that directly maps continuous CAD geometry—represented as sequences of NURBS patches—to physical fields without requiring meshing. It tokenizes control points, knot vectors, and weights to predict continuous surface and volume fields at arbitrary query locations. Evaluated on automotive and aircraft aerodynamics benchmarks, CANTO achieves state‑of‑the‑art accuracy, notably a 19.8% reduction in surface‑pressure error on HiLiftAeroML, and enables gradient‑based inverse design, finding lower‑drag designs on AhmedML.
arXiv AI
Sep 30
CAD-Native Transformer Operators for AI-Aided Engineering
arXiv:2609.36806v1 Announce Type: new Abstract: Modern engineering systems, from automobiles to aircraft, are designed by using precise, continuous parametric computer-aided design (CAD) models. Eval...
arXiv Machine Learning
Sep 22
AgentRouter: Heterogeneous Model Routing for Cost-Optimal Multi-Step Agentic Workflows
arXiv:2609.22951v1 Announce Type: cross Abstract: Enterprise agentic systems that route every trajectory step to a frontier model waste 60-80% of their inference budget on subtasks that smaller model...
arXiv AI
Aug 10
Vehicle routing problem using deep reinforcement learning - A case study about truck planning in the industry
arXiv:2608. 06668v1 Announce Type: new Abstract: As an important component of the supply chain industry, transportation has experienced rapid development in the past decade with the assistance of digital platforms and intelligent algorithms.
arXiv Machine Learning
Jul 23
AlphaRoute: Large Language Models as Semantic Optimizers for Multi-Objective Routing
arXiv:2607. 19768v1 Announce Type: new Abstract: Very Large Scale Integration (VLSI) global routing is an NP-hard combinatorial optimization problem requiring signal net assignment across capacity-constrained 3D grids while minimizing congestion, wirelength, and via transitions.
arXiv Machine Learning
Jul 17
Trajectory-Aware Flow Matching for Topology Optimisation
arXiv:2607. 14652v1 Announce Type: new Abstract: Topology optimisation (TO) often requires repeated finite element analysis and sensitivity-based material updates, which can be costly when multiple candidate designs are needed under varying physical and design conditions.
arXiv AI
Oct 12
Hyper Algorithm Design Agent: Evolving Learnable Optimizer from Zero
The paper introduces a dual‑agent framework that automates the evolution of Meta‑Black‑Box Optimization (MetaBBO) algorithms. By treating the design loop as a coding task, a task agent refines the MetaBBO codebase while a hyper agent continually modifies both agents, enabling recursive self‑improvement. The resulting evolved MetaBBO variants outperform current human‑crafted baselines and adapt quickly across different optimization domains, revealing emergent design principles.
arXiv AI
Jul 2
Solution space path planning for supporting en-route air traffic control
arXiv:2607. 00064v1 Announce Type: new Abstract: As technology advances, many path-planning algorithms have been proposed for Air Traffic Management, yet their operational adoption in tactical control remains limited, revealing a misalignment between algorithmic design priorities and air traffic controllers' needs.
arXiv Machine Learning
Aug 17
Dynamic Multi-Depot Vehicle Routing with Online Requests: Event-Driven Transformer--DRL and Rolling-Horizon Benchmarking
arXiv:2608. 13799v1 Announce Type: new Abstract: This paper presents an event-driven learning and benchmarking framework for the Dynamic Multi-Depot Vehicle Routing Problem with progressively revealed requests and evolving vehicle states.