Urban Air Mobility Demand Forecasting: Modeling Evidence from the Case Study of Milan (Italy)
Explore Milan UAM demand forecasts for airport shuttles, intercity services, air taxis, vertiport scenarios, fares, and traveler preferences.
Blog Topic
Machine Learning & Modeling Articles
Research notes on machine learning methods, forecasting, decision models, and data-driven analysis.
Improving Pre-Disaster Air Mobility Planning with Neural Network-Accelerated Genetic Algorithms
A practical explanation of how neural networks can accelerate genetic algorithms for pre-disaster air mobility evacuation planning.
Stochastic Origin-Destination Matrix Forecasting Using Dual-Stage Graph Convolutional, Recurrent Neural Networks
A clear explanation of stochastic OD matrix forecasting with matrix factorization, graph convolutional networks, and recurrent neural networks.
A Demand Forecasting Model for Urban Air Mobility in Chengdu, China
A readable walkthrough of a four-step UAM demand forecasting model for Chengdu, China, with a focus on modal split, shared operations, and 2030 demand.
Symbolic Knowledge Distillation of Large Language Models
A guide to symbolic knowledge distillation for LLMs, including direct, multilevel, and reinforcement-learning approaches.
Milestones in Knowledge Distillation and Large Language Models
A readable timeline of how language models evolved from early symbolic and statistical systems to transformers, LLMs, and symbolic knowledge distillation.
Knowledge Distillation and Its Types
A clear guide to knowledge distillation, including response-based, feature-based, relation-based, and symbolic knowledge distillation.
Data-Driven Structural Fire Risk Prediction for City Properties
Learn how machine learning helps fire departments prioritize inspections using incidents, violations, inspections, and property data.
Predicting demand for air taxi urban aviation services using machine learning algorithms
A practical look at how machine learning can classify air taxi demand across locations and time periods using ride and weather data.
Support vector regression model for flight demand forecasting
A readable explanation of using support vector regression to forecast final airline bookings from early pre-sale booking patterns.
Demand prediction for urban air mobility using deep learning
A readable explanation of using LSTM, GRU, and Transformer models to estimate future Urban Air Mobility demand from taxi-trip data.
Flight Demand Forecasting with Transformers
A practical explanation of using Temporal Fusion Transformers to improve multi-horizon airport departure demand forecasting.
Deep Learning for Flight Demand Forecasting
A readable explanation of using seq2seq and attention-based deep learning models to forecast airport departure demand for strategic planning.
Hybridized neural network and decision tree based classifier for prognostic decision making in breast cancers
A readable explanation of a hybrid radial basis function network and decision tree approach for supporting breast cancer prognosis.
Discovering Symbolic Models from Deep Learning with Inductive Biases
Learn how inductive biases, sparse latent spaces, graph networks, and symbolic regression extract equations from deep models.
Graph Neural Networks
A practical introduction to graph neural networks, message passing, graph network blocks, and relational inductive bias for learning over structured data.
Budding Trees
A practical explanation of Budding Trees, a differentiable decision-tree model where each node can smoothly move between being a leaf and an internal split.
Adaptive Neural Trees
Explore Adaptive Neural Trees, which combine neural representation learning with conditional tree structures and efficient inference.
Soft Decision Trees
A practical explanation of soft decision trees, where internal nodes route examples probabilistically so every leaf can contribute to the final prediction.
Distilling a Neural Network Into a Soft Decision Tree
Learn how neural-network knowledge can be distilled into a soft decision tree to trade some predictive accuracy for greater interpretability.
Challenges in Deep Reinforcement Learning
Explore key deep reinforcement learning challenges, including sample efficiency, generalization, reproducibility, interpretability, safety, and deployment.
Informer: Efficient Transformers for Long Sequence Time-Series Forecasting
Learn how Informer uses ProbSparse attention, attention distilling, and a generative decoder for efficient long-horizon time-series forecasting.