ACT blocks enable neural operators to learn adaptive coordinate systems via differentiable sampling, yielding consistent accuracy gains on PDE benchmarks by reducing spatial misalignment and operator complexity.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
A unified training framework for mesh-based ML surrogates in CFD improves accuracy and long-horizon stability by enforcing spatial derivative consistency via multi-node prediction, using temporal cross-attention correction, and adding 3D rotary positional embeddings.
citing papers explorer
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Adaptive Coordinate Transforms for Neural Operators
ACT blocks enable neural operators to learn adaptive coordinate systems via differentiable sampling, yielding consistent accuracy gains on PDE benchmarks by reducing spatial misalignment and operator complexity.
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Mesh Based Simulations with Spatial and Temporal awareness
A unified training framework for mesh-based ML surrogates in CFD improves accuracy and long-horizon stability by enforcing spatial derivative consistency via multi-node prediction, using temporal cross-attention correction, and adding 3D rotary positional embeddings.