Papers for

industrial simulation engineers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Autoregressive pde model rollouts improved by event triggered corrections

Stabilizing Autoregressive PDE Foundation Model Rollouts with Event-Triggered Context Healing

Abstract: Autoregressive PDE foundation models enable fast full-field forecasting but accumulate errors as predicted states are recursively reused. We introduce Event-Triggered Context Healing (ETCH), which compares forecasts with measurements from 12 cylinder-surface pressure taps. When their discrepancy exceeds a threshold, pressure-conditioned hourglass diffusion transformers reconstruct the velocity--pressure context for subsequent predictions. On 43 RealPDEBench Cylinder trajectories, ETCH reduces velocity and pressure errors from 48.05 and 63.71% to 6.29 and 7.25% for DPOT, and from 74.97 and 76.91% to 5.16 and 6.84% for Poseidon-T, using the same reconstructors and threshold. For DPOT, correcting 14.56% of windows maintains errors close to reconstructing every window and supports average real-time forecasting and correction on an A100 GPU. These results show that sparse physical feedback stabilizes long autoregressive rollouts across PDE foundation models while reducing reconstruction cost.

Sat 26 SeptComputational Engineering, Finance, and Science
The gist
Predicting fluid flow using computer models often becomes less accurate the longer the model runs because small errors build up. The authors propose a method called Event-Triggered Context Healing (ETCH), which checks the model’s predictions against a few real measurements and fixes the predictions when they differ too much. This correction uses a special neural network to rebuild the flow context, greatly reducing errors while needing fixes only about 15% of the time. Their approach allows fast and more reliable predictions without having to correct every step.
Open → 2609.32321v1

TinyTC and PyTorch/Triton boost stencil computing on GPUs

Stencil Computation at the Intersection of AI and HPC

Abstract: Tensor compilers such as TinyTC and OpenAI Triton were originally developed for AI workloads, but the same tiling and memory abstractions can be applied to implement efficient high-order stencils for scientific and industrial applications. We demonstrate this for an 8th-order, 25-point acoustic stencil with boundary conditions over an a demanding-sized grid, targeting GPGPUs, where we compare the hardware-specialized TinyTC implementation with a portable PyTorch/Triton implementation. The target platforms for evaluation include Intel B70, B580, GPU MAX 1550, NVIDIA A100/RTX6000 Blackwell/H100, and AMD MI325x. For instance, on Battlemage B580 TinyTC reaches 15.6 Gpts/s versus 13.5 Gpts/s for PT/Triton under random initialization, while zero-initialized runs reach up to 35.8 Gpts/s due to hardware memory compression. Using roofline and memory-hierarchy profiling, we show that -as expected- performance is predominantly bandwidth-limited and that compiler-managed L1/LSC caching can effectively replace programmer-managed shared-memory staging for this stencil class. Overall, the results position TinyTC as the performance-oriented path on Intel hardware and PyTorch/Triton as a strong portability/productivity baseline for cross-vendor HPC stencil development.

Wed 9 SeptDistributed, Parallel, and Cluster Computing
The gist
Stencil computations are a type of calculation important for science and engineering, often running on powerful GPUs. The authors show that tools originally made for AI, like TinyTC and PyTorch/Triton, can also speed up these stencil calculations. They tested these tools on many different hardware setups, finding that TinyTC is fastest on some Intel chips, while PyTorch/Triton works well across many types of GPUs. They also found that the main limit on speed is how fast data can move in memory, not the calculation speed itself. This helps people choose the best tools for running complex simulations efficiently.
Open → 2609.10368v1