4/7 Validated on three challenging benchmarks:
– 2D Kolmogorov flow (Re = 10⁶)
– 3D Taylor-Green vortex (Re = 1,600)
– 3D turbulent channel flow (Re_τ = 550)

Results accurately reproduce key turbulence statistics including energy spectra, enstrophy, and Reynolds stresses.

3/7 Key ingredients include:
– PirateNet architecture for deep networks
– Causal training strategies
– Self-adaptive loss weighting
– SOAP optimizer for resolving gradient conflicts
– Time-marching with transfer learning

2/7 For the first time, we show that PINNs can simulate fully developed turbulent flows in 2D and 3D by learning solutions directly from the Navier-Stokes equations without training data or computational grids.

2️⃣ Ocean wave dynamics 🌊: 10-day global forecasts at 0.25° resolution that beat IFS HRES-WAM on 86% of targets. Below: Aurora's accurate prediction of wave patterns during Typhoon Nanmadol👇

1️⃣ Tropical cyclone tracking 🌀: First AI model to outperform seven operational forecasting centers across four basins up to 5 days ahead! Example: Aurora correctly predicted Typhoon Doksuri's landfall when others missed 👇

🎉Excited to announce major new breakthroughs in our Aurora foundation model! Our team (@cbodnar.com, @wessel.ai, @megstanley.bsky.social, @a-lucic.bsky.social, Anna Vaughan) has achieved unprecedented results across multiple Earth system forecasting tasks. Here's what's new... 🧵