The field of predictive modeling and optimization is experiencing significant growth, with a focus on developing innovative solutions to complex problems. Recent research has explored the use of machine learning and artificial intelligence techniques to improve the accuracy and efficiency of predictive models, particularly in areas such as vessel trajectory prediction and autonomous vehicle navigation. Additionally, there is a growing trend towards the use of optimization techniques, such as reinforcement learning and mixed-integer programming, to improve the performance of complex systems, including data centers and logistics networks. Noteworthy papers include: A Multi-Modal Knowledge-Enhanced Framework for Vessel Trajectory Prediction, which proposes a novel framework for predicting vessel trajectories using a combination of machine learning and knowledge-based approaches. ACTIVE: Continuous Similarity Search for Vessel Trajectories, which introduces a real-time continuous trajectory similarity search method for vessels, enabling more predictive and forward-looking comparisons. Rack Position Optimization in Large-Scale Heterogeneous Data Centers, which presents a novel two-tier optimization framework using deep reinforcement learning and gradient-based heuristics to optimize rack positioning in data centers.
Advances in Predictive Models and Optimization Techniques
Sources
A Multi-Modal Knowledge-Enhanced Framework for Vessel Trajectory Prediction
A Methodology to extract Geo-Referenced Standard Routes from AIS Data
Late Breaking Results: Breaking Symmetry- Unconventional Placement of Analog Circuits using Multi-Level Multi-Agent Reinforcement Learning
Dynamic Operating System Scheduling Using Double DQN: A Reinforcement Learning Approach to Task Optimization
Fast enumeration of effective mixed transports for recommending shipper collaboration
Rack Position Optimization in Large-Scale Heterogeneous Data Centers
Perturbation-Based Pinning Control Strategy for Enhanced Synchronization in Complex Networks
Carbon and Reliability-Aware Computing for Heterogeneous Data Centers
Design and Validation of an Intention-Aware Probabilistic Framework for Trajectory Prediction: Integrating COLREGS, Grounding Hazards, and Planned Routes
ACTIVE: Continuous Similarity Search for Vessel Trajectories
An Explainable Reconfiguration-Based Optimization Algorithm for Industrial and Reliability-Redundancy Allocation Problems
Virtual Target Trajectory Prediction for Stochastic Targets
Probabilistic Simulation of Aircraft Descent via a Hybrid Physics-Data Approach
Dynamic Directional Routing of Freight in the Physical Internet