<ul><li>Implicit neural representations (INRs) are crucial for speeding up scientific simulations but struggle with complex fields with high-frequency variations.</li><li>Feature-Adaptive INR (FA-INR) is proposed as an effective alternative using cross-attention and memory banks for flexible feature representations.</li><li>FA-INR achieves high fidelity in large-scale simulation datasets while reducing model size, striking a new balance between accuracy and compactness.</li><li>Introducing a coordinate-guided mixture of experts (MoE) enhances feature representation specialization and efficiency in FA-INR.</li></ul>

High-Fidelity Scientific Simulation Surrogates via Adaptive Implicit Neural Representations

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