Matter Sciences

GridPSF adds an inspectable forward constraint to the MambaTM restoration network's training graph: from the observed clip and the host's latent field it predicts an 8×8 grid of 35 piston-free Zernike coefficients per cell, then blends unit-mass point-spread functions rendered by a Fraunhofer model to fit the turbulent observation. On held-out ATSyn-dynamic videos, forward consistency reaches 29.49 dB PSNR and 0.8882 SSIM, 3.95 dB above the 25.54 dB identity floor of using the clean target as the prediction. One validation-selected matched-host checkpoint pair gives +0.419 dB PSNR and +0.0022 SSIM, but LPIPS rises by 0.0017, and a five-seed control detects no average restoration gain.

GridPSF adds an inspectable forward constraint to the MambaTM restoration network's training graph: from the observed clip and the host's latent field it predicts an 8×8 grid of 35 piston-free Zernike coefficients per cell, then blends unit-mass point-spread functions rendered by a Fraunhofer model to fit the turbulent observation. On held-out ATSyn-dynamic videos, forward consistency reaches 29.49 dB PSNR and 0.8882 SSIM, 3.95 dB above the 25.54 dB identity floor of using the clean target as the prediction. One validation-selected matched-host checkpoint pair gives +0.419 dB PSNR and +0.0022 SSIM, but LPIPS rises by 0.0017, and a five-seed control detects no average restoration gain.

On 5,357 structurally resolved small molecules from DrugBank 5.1.10, Schrödinger Bridge Onsager Flow drives the transport from a uniform chemical library toward a drug-likeness distribution to a total variation distance of 0.0141, against 0.1753 for a Sinkhorn baseline at its best setting across a ten-value regularization sweep. The method keeps one scalar potential per molecule updated by mirror descent instead of an all-pairs coupling matrix; its neural amortization, SBOFNet, returns a total variation distance of 0.0380 in a single forward pass with 1.6M parameters, at lower accuracy than the analytic solver.

On 5,357 structurally resolved small molecules from DrugBank 5.1.10, Schrödinger Bridge Onsager Flow drives the transport from a uniform chemical library toward a drug-likeness distribution to a total variation distance of 0.0141, against 0.1753 for a Sinkhorn baseline at its best setting across a ten-value regularization sweep. The method keeps one scalar potential per molecule updated by mirror descent instead of an all-pairs coupling matrix; its neural amortization, SBOFNet, returns a total variation distance of 0.0380 in a single forward pass with 1.6M parameters, at lower accuracy than the analytic solver.

Convolutional VocalTrax replaces the sample-by-sample tract recursion in differentiable articulatory synthesis with fixed-block convolution: it computes the exact frequency response of the same waveguide and applies it as a causal convolution in 5.8 ms blocks, so gradients pass through FFTs and a 43-step recursion along the tract. On 600 AudioMNIST recordings, the tract forward and backward pass runs 47 to 63 times faster on an A100, and a wav2vec 2.0 classifier trained only on real speech recognizes its reconstructions 14.5% of the time versus 10.0% for VocalTrax, although the log-mel distance is 11% higher.

Convolutional VocalTrax replaces the sample-by-sample tract recursion in differentiable articulatory synthesis with fixed-block convolution: it computes the exact frequency response of the same waveguide and applies it as a causal convolution in 5.8 ms blocks, so gradients pass through FFTs and a 43-step recursion along the tract. On 600 AudioMNIST recordings, the tract forward and backward pass runs 47 to 63 times faster on an A100, and a wav2vec 2.0 classifier trained only on real speech recognizes its reconstructions 14.5% of the time versus 10.0% for VocalTrax, although the log-mel distance is 11% higher.

Particle-resolved granular flow simulation has been limited by repeated information passing over the full particle graph. HCG-GNN moves 5 of its 7 message-passing steps onto two levels of coarse graphs built from occupied cells, matching the multi-step accuracy of a conventional particle-level GNN in inclined-plane granular flow benchmarks while training 6.47 times faster with 46.2% less GPU memory; a model trained on up to 5000 grains predicted a 2 s release of 10000 grains in 31 s, versus 2000 s for DEM under the same benchmark conditions.

Particle-resolved granular flow simulation has been limited by repeated information passing over the full particle graph. HCG-GNN moves 5 of its 7 message-passing steps onto two levels of coarse graphs built from occupied cells, matching the multi-step accuracy of a conventional particle-level GNN in inclined-plane granular flow benchmarks while training 6.47 times faster with 46.2% less GPU memory; a model trained on up to 5000 grains predicted a 2 s release of 10000 grains in 31 s, versus 2000 s for DEM under the same benchmark conditions.

On the Aharonov-Casher continuum model, neural quantum state variational Monte Carlo accommodates both fractional Chern insulators and generalized Wigner crystals and yields phase diagrams at fractional filling for up to 36 electrons with strong band mixing. Two wave functions, a flux-attachment form and a Slater-Jastrow form, describe topological order and crystalline order respectively, and their energies match multiband exact diagonalization on small systems. As the quantum geometry becomes more nonuniform, the Laughlin-like state at 1/3 filling keeps nearly constant energy while the Jain-like state at 2/5 filling drops, so the two respond differently to discrete translation symmetry.

On the Aharonov-Casher continuum model, neural quantum state variational Monte Carlo accommodates both fractional Chern insulators and generalized Wigner crystals and yields phase diagrams at fractional filling for up to 36 electrons with strong band mixing. Two wave functions, a flux-attachment form and a Slater-Jastrow form, describe topological order and crystalline order respectively, and their energies match multiband exact diagonalization on small systems. As the quantum geometry becomes more nonuniform, the Laughlin-like state at 1/3 filling keeps nearly constant energy while the Jain-like state at 2/5 filling drops, so the two respond differently to discrete translation symmetry.

A single time-resolved micro-CT experiment can yield tens to hundreds of three-dimensional images, and segmenting each one independently lets errors drift between frames and be mistaken for physical change. PoreTimeNet feeds several images from one temporal window through a shared spatial encoder and borrows information across time steps with temporal attention and decoder fusion. On a carbonate dissolution sequence its mean absolute porosity error was 0.28 percentage points against 0.97 for a 3D model that ignores temporal information, and rock boundary F1 rose from 0.833 to 0.941.

A single time-resolved micro-CT experiment can yield tens to hundreds of three-dimensional images, and segmenting each one independently lets errors drift between frames and be mistaken for physical change. PoreTimeNet feeds several images from one temporal window through a shared spatial encoder and borrows information across time steps with temporal attention and decoder fusion. On a carbonate dissolution sequence its mean absolute porosity error was 0.28 percentage points against 0.97 for a 3D model that ignores temporal information, and rock boundary F1 rose from 0.833 to 0.941.

DINGO-TGR infers binary parameters and six post-Newtonian phase-deformation parameters jointly with neural posterior estimation, then reweights the network's approximate posterior by importance sampling. For GW150914 the inference takes about 38 minutes against roughly 177 hours for bilby-tgr with dynesty, and about 4.4 hours against 238 hours for the higher signal-to-noise GW250114. GW150914 shows a mild offset from general relativity in the leading principal-component mode, but 115 of 1,000 general-relativity injections resembling GW150914 reach or exceed that offset, an empirical upper-tail p-value of about 0.115 that does not reject the general-relativity null at the 5% level.

DINGO-TGR infers binary parameters and six post-Newtonian phase-deformation parameters jointly with neural posterior estimation, then reweights the network's approximate posterior by importance sampling. For GW150914 the inference takes about 38 minutes against roughly 177 hours for bilby-tgr with dynesty, and about 4.4 hours against 238 hours for the higher signal-to-noise GW250114. GW150914 shows a mild offset from general relativity in the leading principal-component mode, but 115 of 1,000 general-relativity injections resembling GW150914 reach or exceed that offset, an empirical upper-tail p-value of about 0.115 that does not reject the general-relativity null at the 5% level.

A non-convex objective is written into the potential term of a photonic waveguide array, where light evolves by quantum tunneling and the output intensity distribution marks where the minima sit. Experiments ran on one- and two-dimensional waveguide arrays, preparing a low-energy input state with incomplete local annealing and then adding a varying Hamiltonian to speed the search and resolve the global minimum. The authors report quantum speedup over classical stochastic gradient descent, though the main text frames that comparison as an illustrative exploration-behavior baseline.

A non-convex objective is written into the potential term of a photonic waveguide array, where light evolves by quantum tunneling and the output intensity distribution marks where the minima sit. Experiments ran on one- and two-dimensional waveguide arrays, preparing a low-energy input state with incomplete local annealing and then adding a varying Hamiltonian to speed the search and resolve the global minimum. The authors report quantum speedup over classical stochastic gradient descent, though the main text frames that comparison as an illustrative exploration-behavior baseline.

Constitutive model discovery now asks which environmental variable drives which mechanism, not merely which candidate terms fit the response. A constitutive model with internal variables is written as a time-unrolled structural causal model, candidate graphs are scored by a weak-form equilibrium gap through the boundary value problem, and climate-chamber protocols count as interventions. On a synthetic perforated plate at about 0.01 px displacement noise, interventional data recover the true graph exactly in 7 of 10 runs, with no attribution error; correlated observational data never do.

Constitutive model discovery now asks which environmental variable drives which mechanism, not merely which candidate terms fit the response. A constitutive model with internal variables is written as a time-unrolled structural causal model, candidate graphs are scored by a weak-form equilibrium gap through the boundary value problem, and climate-chamber protocols count as interventions. On a synthetic perforated plate at about 0.01 px displacement noise, interventional data recover the true graph exactly in 7 of 10 runs, with no attribution error; correlated observational data never do.