KAN-SAE applies nonlinear per-feature B-spline activations in sparse autoencoders to discover 72% more alive climate features and interpretable patterns such as European heatwaves and Pacific typhoons in deep learning weather models.
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Climax: A foundation model for weather and climate
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Self-supervised Perceiver-VAE pre-trained on 227,000 light curves from MMT-9 and fine-tuned on simulators achieves 85% accuracy and 0.92-0.95 ROC AUC in anomaly detection and motion mode prediction for space objects.
ML climate emulators degrade under seasonal distribution shifts that proxy long-term climate change, but physically motivated compositional decompositions improve out-of-distribution performance with modest in-distribution trade-offs.
SAFE-SVD introduces a sensitivity-aware fidelity-enforcing SVD framework for compressing physics foundation models that maintains higher accuracy than standard methods at greater compression ratios.
Q-SRDRN multi-quantile network with pinball loss and per-quantile heads detects extreme precipitation events up to 18 times more effectively than deterministic baselines while preserving augmentation benefits for the median.
MixINN combines mixed models and deep learning to predict genotype-environment interactions in corn trials, yielding 5.8-7.2% higher average yields when selecting top-performing genotypes compared to standard methods.
DW-Net improves the accuracy versus computational cost Pareto front over standard U-Nets for 2D and 3D multi-scale flow benchmarks by stacking multiple waves while keeping training settings identical.
A multimodal SwinV2-UNet vision transformer conditioned on data modality and time predicts spatiotemporal fluid flows and reconstructs unobserved fields from limited views using CFD data of argon jet injection.
GSNO uses position-dependent spherical Green's functions to create flexible neural operators that adapt to non-equivariant systems on spheres while keeping spectral efficiency and grid invariance.
GAIR introduces a geo-aligned implicit representation module inside a multi-encoder contrastive SSL framework that produces location-aware embeddings and outperforms prior geo-foundation models on 22 geospatial datasets across 9 tasks.
PINN-Cast combines continuous-depth Neural ODEs inside transformer blocks with a two-branch attention module and physics-informed loss to produce short-term weather forecasts that respect governing physical principles.
Scaling laws for weather models exhibit strong cross-channel and cross-horizon heterogeneity, where globally pooled metrics appear favorable while many individual channels degrade at longer leads.
The paper delivers a unified review and roadmap of Earth science foundation models, structured by capability depth from perception to agentic reasoning and by application breadth across atmosphere, hydrosphere, lithosphere, biosphere, anthroposphere, and cryosphere, while compiling over 200 datasets
AI methods can strengthen cross-domain interactions and support more coherent multi-component representations in Earth system models.
Position paper claims multimodal LLMs can significantly advance scientific reasoning and proposes a four-stage roadmap plus challenges and suggestions.
citing papers explorer
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Beyond Linear Superposition: Discovering Climate Features in AI Weather Models with KAN-SAE
KAN-SAE applies nonlinear per-feature B-spline activations in sparse autoencoders to discover 72% more alive climate features and interpretable patterns such as European heatwaves and Pacific typhoons in deep learning weather models.
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A Self-Supervised Framework for Space Object Behaviour Characterisation
Self-supervised Perceiver-VAE pre-trained on 227,000 light curves from MMT-9 and fine-tuned on simulators achieves 85% accuracy and 0.92-0.95 ROC AUC in anomaly detection and motion mode prediction for space objects.
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No Epoch Like the Present: Robust Climate Emulation Requires Out-of-Distribution Generalisation
ML climate emulators degrade under seasonal distribution shifts that proxy long-term climate change, but physically motivated compositional decompositions improve out-of-distribution performance with modest in-distribution trade-offs.
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SAFE-SVD: Sensitivity-Aware Fidelity-Enforcing SVD for Physics Foundation Models
SAFE-SVD introduces a sensitivity-aware fidelity-enforcing SVD framework for compressing physics foundation models that maintains higher accuracy than standard methods at greater compression ratios.
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Multi-Quantile Regression for Extreme Precipitation Downscaling
Q-SRDRN multi-quantile network with pinball loss and per-quantile heads detects extreme precipitation events up to 18 times more effectively than deterministic baselines while preserving augmentation benefits for the median.
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MixINN: Accelerating Plant Breeding by Combining Mixed Models and Deep Learning for Interaction Prediction
MixINN combines mixed models and deep learning to predict genotype-environment interactions in corn trials, yielding 5.8-7.2% higher average yields when selecting top-performing genotypes compared to standard methods.
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Deep Wave Network for Modeling Multi-Scale Physical Dynamics
DW-Net improves the accuracy versus computational cost Pareto front over standard U-Nets for 2D and 3D multi-scale flow benchmarks by stacking multiple waves while keeping training settings identical.
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A Multimodal Vision Transformer-based Modeling Framework for Prediction of Fluid Flows in Energy Systems
A multimodal SwinV2-UNet vision transformer conditioned on data modality and time predicts spatiotemporal fluid flows and reconstructs unobserved fields from limited views using CFD data of argon jet injection.
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Generalized Spherical Neural Operators: Green's Function Formulation
GSNO uses position-dependent spherical Green's functions to create flexible neural operators that adapt to non-equivariant systems on spheres while keeping spectral efficiency and grid invariance.
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GAIR: Location-Aware Self-Supervised Contrastive Pre-Training with Geo-Aligned Implicit Representations
GAIR introduces a geo-aligned implicit representation module inside a multi-encoder contrastive SSL framework that produces location-aware embeddings and outperforms prior geo-foundation models on 22 geospatial datasets across 9 tasks.
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PINN-Cast: Exploring the Role of Continuous-Depth NODE in Transformers and Physics Informed Loss as Soft Physical Constraints in Short-term Weather Forecasting
PINN-Cast combines continuous-depth Neural ODEs inside transformer blocks with a two-branch attention module and physics-informed loss to produce short-term weather forecasts that respect governing physical principles.
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Towards Scaling Law Analysis For Spatiotemporal Weather Data
Scaling laws for weather models exhibit strong cross-channel and cross-horizon heterogeneity, where globally pooled metrics appear favorable while many individual channels degrade at longer leads.
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Earth Science Foundation Models: From Perception to Reasoning and Discovery
The paper delivers a unified review and roadmap of Earth science foundation models, structured by capability depth from perception to agentic reasoning and by application breadth across atmosphere, hydrosphere, lithosphere, biosphere, anthroposphere, and cryosphere, while compiling over 200 datasets
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Toward Artificial Intelligence Enabled Earth System Coupling
AI methods can strengthen cross-domain interactions and support more coherent multi-component representations in Earth system models.
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Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning
Position paper claims multimodal LLMs can significantly advance scientific reasoning and proposes a four-stage roadmap plus challenges and suggestions.
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