Lagrangian Gaussian Processes use discrete Euler-Lagrange equations to condition GPs, preserving geometric structure for stable dynamics learning from sparse position snapshots without velocities.
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A dimensional type system extends Hindley-Milner inference with abelian-group constraints and carries annotations through MLIR lowering to jointly decide numeric representation and deterministic memory allocation at compile time.
Fixed-reservoir QRC achieves 81% lower test MSE and 52,000x faster training than variational QPINN on Lorenz chaotic prediction with 4-5 qubits.
An auxiliary finite-difference regularizer on residual gradients improves outer-wall flux and boundary-condition accuracy in a 3D annular heat-conduction PINN benchmark when aligned with the quantity of interest.
A type system over finitely generated abelian groups enables design-time verification of AI model properties and links Hindley-Milner unification to a restriction of Solomonoff's universal prior.
citing papers explorer
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Structure-Preserving Gaussian Processes Via Discrete Euler-Lagrange Equations
Lagrangian Gaussian Processes use discrete Euler-Lagrange equations to condition GPs, preserving geometric structure for stable dynamics learning from sparse position snapshots without velocities.
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Dimensional Type Systems and Deterministic Memory Management: Design-Time Semantic Preservation in Native Compilation
A dimensional type system extends Hindley-Milner inference with abelian-group constraints and carries annotations through MLIR lowering to jointly decide numeric representation and deterministic memory allocation at compile time.
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Fixed-Reservoir vs Variational Quantum Architectures for Chaotic Dynamics: Benchmarking QRC and QPINN on the Lorenz System
Fixed-reservoir QRC achieves 81% lower test MSE and 52,000x faster training than variational QPINN on Lorenz chaotic prediction with 4-5 qubits.
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Auxiliary Finite-Difference Residual-Gradient Regularization for PINNs
An auxiliary finite-difference regularizer on residual gradients improves outer-wall flux and boundary-condition accuracy in a 3D annular heat-conduction PINN benchmark when aligned with the quantity of interest.
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Decidable By Construction: Design-Time Verification for Trustworthy AI
A type system over finitely generated abelian groups enables design-time verification of AI model properties and links Hindley-Milner unification to a restriction of Solomonoff's universal prior.