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On the show
Recent episodes
Target Concept Tuning: Solving the AI Blindspot in Extreme Weather Forecasting
Mar 24, 2026
0m 18s
NeuralGCM: Observation-Based Hybrid Modeling for Global Precipitation Forecasting
Jan 15, 2026
15m 02s
Flow-Matched Neural Operators for Continuous PDE Dynamics
Dec 9, 2025
12m 02s
Ep. 11: Principals of Diffusion Models
Nov 5, 2025
17m 55s
Ep 10. RainSeer: Physics-Guided Fine-Grained Rainfall Reconstruction
Oct 9, 2025
19m 03s
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| Date | Episode | Topics | Guests | Brands | Places | Keywords | Sponsor | Length | |
|---|---|---|---|---|---|---|---|---|---|
| 3/24/26 | Target Concept Tuning: Solving the AI Blindspot in Extreme Weather Forecasting✨ | AIextreme weather+3 | — | Pangu-WeatherTaCT+2 | — | Target Concept TuningTaCT+3 | — | 0m 18s | |
| 1/15/26 | NeuralGCM: Observation-Based Hybrid Modeling for Global Precipitation Forecasting✨ | NeuralGCMmachine learning+10 | — | NeuralGCMCMIP6+3 | — | hybrid atmospheric modelobservational data+2 | — | 15m 02s | |
| 12/9/25 | Flow-Matched Neural Operators for Continuous PDE Dynamics✨ | neural operatorspartial differential equations+2 | — | the Continuous Flow Operator (CFO)CFO | — | Continuous Flow Operatorflow matching objective+3 | — | 12m 02s | |
| 11/5/25 | Ep. 11: Principals of Diffusion Models✨ | diffusion modelsVariational View+5 | — | DPM-SolverarXiv+1 | — | mathematical equivalencetractable training objectives+5 | — | 17m 55s | |
| 10/9/25 | Ep 10. RainSeer: Physics-Guided Fine-Grained Rainfall Reconstruction✨ | RainSeerrainfall reconstruction+4 | — | RainSeera Geo-Aware Rain Decoder | — | high-resolutionstructure-aware framework+2 | — | 19m 03s | |
| 9/21/25 | Ep. 9: FlowCast-ODE Cntinuous Hourly Weather Forecasting with Dynamic Flow Matching and ODE Integration✨ | weather forecastingdeep learning+2 | — | FlowCast-ODEERA5+1 | — | FlowCast-ODEhourly forecasting+5 | — | 15m 59s | |
| 9/3/25 |  Ep.8 AQUAH: An Automatic Quantification and Unified Agent in Hydrology✨ | hydrologywater resource management+2 | — | AQUAHClaude-Sonnet-4,+1 | Little Bighorn basinUnited States+1 | AQUAHend-to-end agent+3 | — | 15m 58s | |
| 8/5/25 | Ep 7. cBottle: Climate in a bottle - foundational AI weather prediction✨ | climate predictionAI+2 | — | cBottleERA5+2 | Earth | NVIDIAgenerative diffusion+2 | — | 19m 57s | |
| 6/30/25 | Ep.6 How to fine tune a weather foundation model to hydrological variables?✨ | weather foundation modelhydrological variables+3 | — | Aurora weather foundation modelAurora+1 | AuroraEarth | Aurorafine-tuning+3 | — | 10m 11s | |
| 6/3/25 |  Ep.5 What is foundation model - drawing from numerical simulation✨ | foundation modelsnumerical simulation+3 | — | Data-Driven Finite Element MethodarXiv+1 | — | computational sciencemodel building+1 | — | 29m 09s | |
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| 5/7/25 |  Ep.4 Any-to-any Earth Observation Generation and Thinking - TerraMind | IBM recently released the first-of-its-kind geospatial intelligence any-to-any model TerraMind. In this podcast, we feature this new generative model and learn its capability of multi-modality. I believe there is a lot of potential with such a model.Jakubik, J., Yang, F., Blumenstiel, B., Scheurer, E., Sedona, R., Maurogiovanni, S., Bosmans, J., Dionelis, N., Marsocci, V., Kopp, N., Ramachandran, R., Fraccaro, P., Brunschwiler, T., Cavallaro, G., & Longépé, N. (2025). TerraMind: Large-Scale Generative Multimodality for Earth Observation. ArXiv. https://arxiv.org/abs/2504.11171 | 24m 25s | ||||||
| 4/24/25 |  Ep.3 Geospatial foundation model - Prithvi | Today, we are featuring a geospatial foundation model Prithvi, produced by NASA and IBM, one of the first foundation model in this space. Trained on a large global dataset of NASA’s Harmonized Landsat and Sentinel-2 data, Prithvi-EO-2.0 demonstrates significant improvements over its predecessor by incorporating temporal and location embeddings. Through extensive benchmarking using GEO-Bench, it outperforms other prominent GFMs across various remote sensing tasks and resolutions, highlighting its versatility. Furthermore, the model has been successfully applied to real-world downstream tasks led by subject matter experts in areas such as disaster response, land use and crop mapping, and ecosystem dynamics monitoring, showcasing its practical utility. Emphasising a Trusted Open Science approach, Prithvi-EO-2.0 is made available on Hugging Face and IBM TerraTorch to facilitate community adoption and customization, aiming to overcome limitations of previous GFMs related to multi-temporality, validation, and ease of use for non-AI experts. | 23m 28s | ||||||
| 4/15/25 |  Ep.2 AI models for flood forecasting - HydrographNet | This research article introduces HydroGraphNet, a novel physics-informed graph neural network for improved flood forecasting. Traditional hydrodynamic models are computationally expensive, while machine learning alternatives often lack physical accuracy and interpretability. HydroGraphNet integrates the Kolmogorov–Arnold Network (KAN) to enhance model interpretability within an unstructured mesh framework. By embedding mass conservation laws into its training and using a specific architecture, the model achieves more physically consistent and accurate predictions. Validation on real-world flood data demonstrates significant reductions in prediction error and improvements in identifying major flood events compared to standard methods.Taghizadeh, M., Zandsalimi, Z., Nabian, M. A., Shafiee-Jood, M., & Alemazkoor, N. Interpretable physics-informed graph neural networks for flood forecasting. Computer-Aided Civil and Infrastructure Engineering. https://doi.org/10.1111/mice.13484 | 22m 26s | ||||||
| 3/31/25 |  Ep.1 AI models for weather forecasting | We are featuring three papers:Mardani, M., Brenowitz, N., Cohen, Y., Pathak, J., Chen, C., Liu, C., Vahdat, A., Nabian, M. A., Ge, T., Subramaniam, A., Kashinath, K., Kautz, J., & Pritchard, M. (2025). Residual corrective diffusion modeling for km-scale atmospheric downscaling. Communications Earth & Environment, 6(1), 1-10. https://doi.org/10.1038/s43247-025-02042-5Price, I., Alet, F., Andersson, T. R., Masters, D., Ewalds, T., Stott, J., Mohamed, S., Battaglia, P., Lam, R., & Willson, M. (2025). Probabilistic weather forecasting with machine learning. Nature, 637(8044), 84-90. https://doi.org/10.1038/s41586-024-08252-9Lam, R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., Alet, F., Ravuri, S., Ewalds, T., Eaton-Rosen, Z., Hu, W., Merose, A., Hoyer, S., Holland, G., Vinyals, O., Stott, J., Pritzel, A., Mohamed, S., & Battaglia, P. (2023). Learning skillful medium-range global weather forecasting. Science. https://doi.org/adi2336 | 22m 16s | ||||||
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Chart Positions
4 placements across 4 markets.
Chart Positions
4 placements across 4 markets.