Smarter Turbulence Models: Harnessing Data and Symmetry
![At an initial time, Large Eddy Simulation (LES) solutions align with filtered Direct Numerical Simulation (DNS) data; however, for the Clark model, this alignment breaks down around [latex]t = 2.1[/latex], leading to computational instability and preventing solution completion.](https://arxiv.org/html/2603.05325v1/2603.05325v1/figures/snellius/velocities-z.png)
New research explores how data-driven approaches, particularly those respecting fundamental physical symmetries, can improve the accuracy of large-eddy simulations.
Science
Shrinking the Spectrums: Efficient Deep Learning for Hyperspectral Imagery
![The research details the implementation of convolutional neural networks across varying dimensionalities-one-dimensional for spectral analysis [latex]CNN1D[/latex], two-dimensional for spatial analysis [latex]CNN2D[/latex], and three-dimensional to integrate both spectral and spatial information [latex]CNN3D[/latex]-demonstrating a progression in model complexity for comprehensive data interpretation.](https://arxiv.org/html/2603.04720v1/2603.04720v1/cnn.png)
A new study systematically evaluates techniques to compress deep learning models, making them more practical for land cover classification using complex hyperspectral data.
Predicting Nuclear Reactions with Confidence
![The BNN-I6 model accurately predicts (n,p) reaction cross sections across a wide range of nuclei, with root-mean-square deviations [latex]\sigma_{rms}[/latex] generally remaining small-indicating successful capture of the systematic dependence on neutron and proton number-though slightly larger deviations emerge for mid-mass and heavy nuclei, potentially due to increased structural complexity and limited experimental data in those regions.](https://arxiv.org/html/2603.04789v1/2603.04789v1/x6.png)
A new machine learning approach leverages Bayesian Neural Networks to accurately forecast neutron-induced reaction probabilities and, crucially, quantify the uncertainty in those predictions.
Seeing the Future: AI Predicts Rainfall with Greater Accuracy
A new deep learning model leverages multimodal weather data and physical principles to significantly improve short-term precipitation forecasting.
Radar-Guided Jamming: Securing Wireless Communications with Sensing
A new framework uses radar sensing to intelligently disrupt eavesdropping attempts, enhancing the security of integrated sensing and communication systems.
Decoding Deception: An AI That Helps You Spot Manipulation
Researchers have developed an agentic AI framework that moves beyond simple detection to offer users self-discovery prompts when manipulative communication patterns are identified in ongoing conversations.
Pinpointing Policy Impacts: A New Bayesian Approach
A novel statistical method offers a more robust way to evaluate the effectiveness of climate policies amidst complex and shifting economic landscapes.
Seeing the City: Real-Time Traffic Insights at Scale
A new edge-cloud framework unlocks real-time analysis of thousands of video streams, providing unprecedented visibility into urban traffic patterns.
Coordinated Motion: AI Navigates Crowded Spaces with Graph Networks
A new approach combines global planning with attentive graph neural networks to enable safer and more efficient navigation for multiple robots in dynamic environments.
The Forgetting Curve of AI: Why Neural Networks Lose Memories
New research reveals the underlying mechanisms driving catastrophic forgetting in artificial intelligence, offering insights into how to build more stable and adaptable learning systems.