A high-performance PDE neural solver based on Manifold Function Encoder, featuring resolution-independent memory footprint and arbitrary PDE input support for large-scale physical simulations.
See LICENSE fileNeural OperatorPDE SolverMemory Efficiency
A benchmark dataset for PDE surrogate tasks within the ai4sci-tasks evaluation framework, containing training trajectories and evaluation splits for assessing neural operator and surrogate modeling performance.
See LICENSE filePDE SurrogateBenchmark DatasetScientific ML
A survey of data-driven and physics-informed neural operators, organizing the architecture landscape (DeepONet / FNO / GNO / PINO / Transformer-based / diffusion) along three axes: scalability, generalizability, and data sensitivity.
arXiv preprint (copyright held by authors)Neural OperatorScientific MLDeepONet
Proposes Transformer-based learning of structured representations for symbolic ODE expressions, preserving commutative properties to enable mathematical reasoning in symbolic form, distinct from traditional methods relying on numerical fitting.
See LICENSE fileSymbolic ComputationODETransformer
A 36M-parameter UNet-based PDE solver v2 by Los Alamos National Laboratory, designed for surrogate modeling of partial differential equations in scientific computing.
A provenance-bearing benchmark for verifying symbolic candidate solutions to PDEs, containing 20 records with generator outputs, verification cases, and human annotations for assessing symbolic solver reliability.
Proposes a collaborative framework of AI virtual labs under a citation-based influence economy to automate neural operator architecture search and training evaluation.
See LICENSE fileNeural OperatorAI AgentAutomated Scientific Discovery
Proposes an adaptive memory gating mechanism enabling neural operators to dynamically adjust memory weights based on observation conditions, improving adaptability in solving time-dependent PDEs.
See LICENSE fileNeural OperatorsAdaptive MemoryTime-dependent PDE
Proposes Adaptive Operator Transformation (AOT) to address structural diversity challenges in multi-PDE pre-training by adapting target solution operators rather than increasing model capacity, improving neural operator generalization across equations.
See LICENSE fileNeural OperatorPDE Pre-trainingAdaptive Transformation
