Proves O((α_n n)^{-1/2}) convergence of GNDE trajectories and adjoints to Graphon-NDE limits on sparse random graphs, with DTO/OTD consistency and experimental support for zero-shot transfer.
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On Neural Differential Equations
27 Pith papers cite this work, alongside 120 external citations. Polarity classification is still indexing.
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A JAX-based differentiable model of pressure vacuum swing adsorption accelerates cyclic steady-state simulation by 20x via Newton iteration and produces a better Pareto front with IPOPT than NSGA-II in two orders of magnitude less time on a post-combustion capture benchmark.
Differentiable implosion modeling enables gradient-based optimization of 500-parameter laser pulses for 25 kJ direct-drive ICF implosions on OMEGA-scale targets.
LIMINAL fits nested Lindblad models to tomographic data and uses likelihood-ratio tests to identify minimal dynamics for a five-qubit superconducting processor, supporting three-local Hamiltonian terms and two-local dissipation but not three-local dissipation.
Flow matching on time series targets a closed-form nonparametric velocity field that is a similarity-weighted mixture of observed transition velocities, making neural models approximations to an ideal memory-augmented dynamical system sampler.
AMIGO is an end-to-end differentiable forward model of JWST AMI that corrects detector systematics to recover high-precision astrometry and detect close high-contrast companions.
Extends scattered-wave-function and open-worldline-instanton methods to multidimensional curved spacetimes and demonstrates agreement on 2D metric examples.
A framework for online forecast reconciliation is developed via multivariate linear models on graph hierarchies, ridge regression, and recursive least squares, with a demonstration on district heating load data.
GaRA generates task-specific LoRA weight updates conditioned on graph structures to enable better whole-graph encoding in LLMs for zero-shot graph learning.
A Latent NCDE-based continuous-time probabilistic corrector wrapped around deterministic physics propagators like GMAT improves forecast accuracy and produces sharp calibrated full-covariance uncertainty estimates on real CDDIS data for 2-4 day horizons.
Stochastic Lifting adds random labels to training transitions to train a regression model that generates diverse stochastic trajectories without collapsing to mean predictions.
The Inverter framework formalizes inverse learning to generate coherent multi-step trajectories, outperforming offline RL and diffusion baselines on D4RL maze tasks by 24% on average with 10-100x less inference time while also matching GRAPE fidelity on single-qubit gates at >1000x speed.
NHODE framework learns partially observed dynamical systems by combining Hamiltonian neural networks with neural ODEs, enforcing energy conservation and improving long-horizon stability over data-driven baselines on mass-spring and three-body problems.
Derives RAM, a reward-adjusted consistency loss extending diffusion pretraining regression to efficient KL-regularized RL post-training, achieving peak rewards up to 50x faster than Flow-GRPO on Stable Diffusion 3.5M.
NSP model fuses satellite and gauge data with neural processes and SDEs, outperforming 13 baselines and JAXA's operational product on a new 43k-sample US benchmark across six metrics.
A pathwise Zakai-equation control formulation is used to train conditional neural SDEs that amortize nonlinear filtering of partially observed stochastic dynamics.
Neural CDEs serve as correctors that reduce error accumulation in multi-step forecasts from learned time-series models across synthetic, physics, and real-world data.
Global multiqubit Rydberg gates enable break-even measurement-free QEC and lower-shuttling Floquet codes in neutral-atom hardware.
ShockCast is a two-phase ML method that predicts adaptive timestep sizes to model high-speed flows with shocks more efficiently than fixed-step approaches.
FNODE projects Neural ODE dynamics into the frequency domain via FFT and reports better generalization and convergence stability than GRUs, LSTMs, and ANODE on Lotka-Volterra, forced Duffing, Van der Pol, and Lorenz systems.
Introduces models for neural ODEs trained with online SGD and derives their high-dimensional learning curves via dynamical mean field theory.
Neural networks can detect 38% of summer hypoxic events shelf-wide from satellites with 47% precision, but only within the homogeneous mixed layer.
MPG-NODEs identify power system dynamics more flexibly than standard neural ODEs by using graph message passing, enabling transfer learning for adding or removing lines and units.
A physics-constrained inverse-problem framework identifies graph-based lumped-parameter thermal models from temperature measurements for spacecraft digital-twin applications.
citing papers explorer
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Accelerating Simulation and Optimisation of Cyclic Adsorption Processes with Differentiable Programming
A JAX-based differentiable model of pressure vacuum swing adsorption accelerates cyclic steady-state simulation by 20x via Newton iteration and produces a better Pareto front with IPOPT than NSGA-II in two orders of magnitude less time on a post-combustion capture benchmark.