Search papers, labs, and topics across Lattice.
87 papers published across 4 labs.
VAE-GANs let you have your cake and eat it too: high-fidelity geological models *and* accurate history matching in reservoir simulation, something previous DL methods couldn't deliver.
Foundation models trained on audio, general time series, and brain signals can be distilled into a single, powerful encoder for scientific time series, unlocking performance gains on par with task-specific training.
Gaussian assumptions about Earth structure introduce bias and significantly under-report moment tensor uncertainties, but simulation-based inference offers a robust alternative for more reliable earthquake source characterization.
LLMs can generate novel mathematical research problems in differential geometry that experts find both unknown and valuable, suggesting a new avenue for AI-assisted mathematical discovery.
Decomposing uncertainty into aleatoric and epistemic components in image segmentation is often misleading due to substantial entanglement, but ensembles offer a surprisingly robust and less entangled alternative.
VAE-GANs let you have your cake and eat it too: high-fidelity geological models *and* accurate history matching in reservoir simulation, something previous DL methods couldn't deliver.
Foundation models trained on audio, general time series, and brain signals can be distilled into a single, powerful encoder for scientific time series, unlocking performance gains on par with task-specific training.
Gaussian assumptions about Earth structure introduce bias and significantly under-report moment tensor uncertainties, but simulation-based inference offers a robust alternative for more reliable earthquake source characterization.
LLMs can generate novel mathematical research problems in differential geometry that experts find both unknown and valuable, suggesting a new avenue for AI-assisted mathematical discovery.
Decomposing uncertainty into aleatoric and epistemic components in image segmentation is often misleading due to substantial entanglement, but ensembles offer a surprisingly robust and less entangled alternative.
Scribble prompts beat point prompts for interactive surgical segmentation, achieving state-of-the-art Dice scores with fewer interactions.
Information-theoretic limits on control performance are now computable even when feedback matters most, thanks to a new bound that self-consistently accounts for the controller's impact on sensor information.
Hypergraph modeling of patient visits, coupled with contrastive pre-training, significantly boosts medication recommendation accuracy and safety by capturing complex relationships missed by traditional graph-based approaches.
Unstable explanations plague ML models on spectroscopy data, but SHAPCA offers a more consistent and interpretable approach by combining PCA and SHAP values in the original input space.
Descriptor-guided sampling and active learning slashes the cost of simulating gas-surface interactions, enabling accurate molecular dynamics at scale.
Forget waiting hours for CFD simulations: this work shows you can get accurate cerebrovascular hemodynamics predictions 100x-1000x faster using reduced-order models based on POD and reservoir computing.
Unlock the power of interpretable AI: SINDy-KANs distills complex neural networks into sparse equations, revealing the underlying dynamics of systems.
Particle physics techniques can give your drone superhuman senses: statistical methods from CERN enable UAVs to detect subtle blade damage with calibrated uncertainty, outperforming standard anomaly detection methods.
Generative AI can now contribute to solving long-standing open problems in arithmetic geometry: this paper details how it helped resolve a question posed by Manin in 1972 regarding R-equivalence on a specific cubic surface.
LLMs can generate significantly more novel and technically rigorous scientific ideas by explicitly learning to reason from motivations to methodologies.
Forget expensive deep-sea expeditions: GEAR finds structurally similar terrestrial environments with surprising accuracy, opening new avenues for biological research.
A single 3D-printed part can replace complex multi-link laparoscopic graspers, slashing manufacturing costs while maintaining reliable bistable actuation.
Rényi divergence may be the missing key to understanding thermal equilibrium in quantum systems, revealing a novel constraint on wavefunction ensembles.
Achieve atomic-scale clarity in noisy HRTEM images with a novel denoising network that intelligently exploits statistical characteristics in both spatial and frequency domains.
Uncover hidden relationships in drug discovery: BVSIMC uses Bayesian variable selection to pinpoint the most relevant chemical and genomic features, boosting prediction accuracy and interpretability.
Achieve near-perfect radio map reconstruction (SSIM 0.9752, PSNR 36.37 dB) from limited data by injecting electromagnetic theory into diffusion models.
Achieve topologically coherent coronary vessel segmentation by directly optimizing for geometric structure, rather than pixel-wise accuracy, using preference-based learning.
Synthesized PET scans from MRI can nearly match the diagnostic accuracy of real PET for Alzheimer's, potentially unlocking wider access to crucial functional insights.
Representing complex 3D biomedical graphs as learned tokens unlocks generative modeling and efficient analysis of anatomical structures.
$GW$ self-energies have a discontinuity at integer particle numbers, explaining why they can give accurate quasiparticle energies despite large delocalization errors in RPA total energies.
Finally, a neural interatomic potential that accurately models long-range electrostatic interactions without sacrificing SO(3) equivariance or energy-force consistency.
Quantum chemistry's killer app isn't just about solving the unsolvable; it's about making routine calculations faster and more accessible.
PINNs can now come with guarantees: vanishing residual error provably ensures convergence to the true PDE solution, bridging the gap between empirical performance and theoretical certainty.
Unlock scalable cardio-sleep insights by repurposing ubiquitous single-lead ECG data for accurate sleep phenotyping, rivalling resource-intensive polysomnography.
Predict thermal warpage in chiplet designs 200x faster than FEM simulations using a physics-aware graph neural network that learns directly from floorplans.
Estimating time-varying reproduction numbers just got more robust: CIRL blends epidemiological constraints with data-driven temporal representations, outperforming traditional methods in noisy, non-stationary conditions.
Forget static embeddings: this paper shows how modeling scientific concepts as evolving complex networks reveals surprising connections between conceptual change and network topology.
This model beats clinical reports in quantitative coronary angiography, opening the door to automated, comprehensive assessment of coronary artery disease at the point of care.
Accurately modeling notoriously flexible protein-glycan interactions is now more tractable thanks to a refined HADDOCK3 protocol that achieves sub-3 angstrom RMSD.
Standard ComBat harmonization methods fall apart when applied to dMRI data containing neurological disorders, but a simple MLP-based outlier compensation fixes the problem.
Quantum computers could finally unlock the full potential of machine learning for drug discovery by directly generating the quantum chemistry data that classical computers struggle to produce.
Seemingly accurate physics-informed surrogates can fail spectacularly when integrated into power system simulations, especially under stress, highlighting the need for rigorous in-simulator validation.
Generate realistic, atom-level molecular dynamics trajectories orders of magnitude faster with a novel State Space Model that captures long-range dependencies in biomolecular systems.
Ditch costly PIDE integration: RHYME-XT learns the flow map directly, offering a continuous-time, discretization-invariant representation that beats state-of-the-art neural operators.
Virtual cell perturbation prediction gets a 12x speedup in pretraining and a 12% boost in biological fidelity with SCALE, a new foundation model that prioritizes scalable infrastructure and biologically faithful evaluation.
Injecting semantic information from related modalities early in the embedding process significantly boosts performance on multimodal medical image classification tasks.
Precisely predicting bubble formation on electrocatalysts unlocks a new level of control over electrolyzer efficiency.
NNVMC's promise for solving quantum many-body problems is currently bottlenecked by surprisingly mundane issues: low-intensity elementwise operations and data movement on GPUs.
LLMs can now automatically generate bug-detection patterns for scientific code, offering a scalable solution to the growing problem of methodology errors in AI-driven research.
See how stochastic fluctuations in a simple Hückel model can bring condensed-phase spectroscopy to life for undergrads.
Drifting offers a surprisingly effective way to distill iterative Boltzmann sampling into a single forward pass, even with unknown normalization constants.
Unlock faster simulations of complex engineering systems with piecewise linear nonlinearities using a new data-driven model order reduction technique based on dynamic mode decomposition.
Convolutional Neural Operators (CNOs) surprisingly excel at capturing translated dynamics in the FitzHugh-Nagumo model, despite other architectures achieving lower training error or faster inference.
Simulate earthquake ground motion 10,000x faster with a new latent operator flow matching method, opening the door to real-time risk assessment for critical infrastructure.
Heuristic maritime routes lead to extreme fuel waste in nearly 5% of voyages, but this RL approach cuts that risk by almost 10x.
Quantum annealing could soon accelerate protein engineering: Q-BIOLAT formulates protein fitness as a QUBO problem, directly compatible with emerging quantum annealing hardware.
Molecular coatings can restructure the electromagnetic vacuum around nanoparticles, enabling strong coupling and Rabi oscillations where they were previously impossible.
Ditch the temperature ladder: Generative Replica Exchange (GREX) uses normalizing flows to generate high-temperature configurations on-demand, slashing the computational cost of replica exchange simulations.
LLMs can disentangle Long COVID pathology from confounding factors like menopause, achieving high precision in predicting disease severity using wearable sensor data.
Forget rigid circuits - this new method seamlessly weaves stretchable sensors directly into clothing using a clever combo of 3D printing and embroidery.
Precise calculations of electron-impact rotational excitation rates for water ion isotopologues provide critical data for modeling astrophysical plasma environments.
A national center focused on AI and robotics in medicine could be the key to unlocking the transformative potential of these technologies in healthcare.
Continuous, high-resolution shape sensing in steerable drilling robots is now possible without directly embedding sensors on the instrument surface, thanks to a clever OFDR-based assembly.
Cavity quantum electrodynamics offers a new knob for tuning bond-breaking processes in molecular materials, as demonstrated by a new method that explicitly includes quantized cavity photons in spin-flip configuration interaction calculations.
A specific Zn-Al alloy (Zn$_{0.9}$Al$_{0.1}$) achieves OER performance rivaling transition-metal catalysts, offering a low-cost alternative for alkaline water splitting.
Tackle previously intractable open quantum systems simulations with TENSO, a new open-source package that efficiently handles complex environments via tree tensor networks.
Unlock higher PEM fuel cell efficiency: in-phase current and temperature oscillations can slash resistivity by eliminating proton transport losses.
Diamond interfaces dramatically alter the phase diagram of high-pressure ice, suggesting that many experimental measurements may be skewed by interface effects.
Quantum field theory offers a fresh perspective on chemical interactions, promising to unlock new insights into reaction manipulation, large-scale system modeling, and unconventional scaling laws beyond the reach of standard quantum mechanics.
Isotopic substitution in water reveals intricate details of vibrational energy transfer and dephasing, offering a new lens into the interplay between intramolecular modes and their thermal environment.
A new prompting strategy closes the gap between general-purpose and specialized cell segmentation models, suggesting a path to more efficient adaptation.
Achieve oracle-level performance in multi-armed sequential hypothesis testing by betting, even without knowing which arm provides the most evidence.
Exploiting geometric symmetries in tensegrity structures slashes computational cost and boosts accuracy in physics-informed neural networks.
Accurately predict urban pollutant dispersion in real-time with a novel data-driven model that's orders of magnitude faster than traditional CFD.
By explicitly modeling pollutant propagation delays with neural delay differential equations, AirDDE significantly improves air quality forecasting accuracy.
Generative models can fail to produce globally consistent counterfactuals when causal graphs have complex topologies, but a novel sheaf-theoretic framework with entropic regularization can overcome these limitations.
Imagine seeing your tongue move in real-time based on the sounds you make – AURORA brings that closer to reality.
LLMs can now recommend drugs with state-of-the-art accuracy by synthesizing individual patient context with the prescribing tendencies of similar cases, outperforming guideline-based and similar-patient retrieval methods.
Neural networks can accurately predict polymer free energies, even when traditional methods like Bennett Acceptance Ratio fail due to poor phase-space overlap.
An AI model can estimate legal age from clavicle CT scans with higher accuracy than human experts, potentially revolutionizing forensic age assessment.
A new prompt-free medical image segmentation model achieves impressive zero-shot and cross-modal transfer performance by explicitly disentangling geometric and semantic anatomical knowledge.
AI spots a hidden pattern in lung scans of lupus patients, revealing that specific airway dilations in the upper lobes could be a telltale sign of interstitial lung disease.
Standard PCA, despite its widespread use in CAD, struggles to directly reveal the original design parameters of a geometry, but this paper identifies conditions for accurate parameter estimation.
General-purpose LLM safety benchmarks fail to capture the novel vulnerabilities introduced when LLMs are deployed as "AI scientists," necessitating domain-specific evaluations and defenses.
Predicting permeability tensors from microstructure images just got 33% more accurate thanks to a physics-informed CNN-Transformer that learns faster and generalizes better via pretraining and differentiable constraints.
By explicitly modeling cardiac pathology, this ECG reconstruction method achieves a 76% reduction in error compared to existing techniques, promising more accurate diagnoses from portable devices.
Medical vision-language models perform better when the modality gap is tuned to an intermediate level, challenging the assumption that minimizing it is always optimal.
Genetic programming can discover unconventional multigrid cycles that outperform hand-tuned methods, suggesting automated algorithm design can unlock untapped performance in classical numerical solvers.
ML's seismic and volcanic signal analysis needs more than just accuracy; it demands reliability under changing conditions, uncertainty quantification, and physically meaningful constraints.
Ultrafast X-ray spectroscopy reveals the hidden choreography of electronic state transitions that drive Norrish Type-I reactions, pinpointing the long-lived $^3n\pi^*$ state as the key player.
By handling input noise directly through Wasserstein distances, \PWAGPs offer a more robust and transparent approach to uncertainty quantification in GP regression compared to latent-input models.
Unlocking insights from massive molecular dynamics simulations just got easier: covariance matrix comparisons reveal key physical properties and phase transitions with remarkable data efficiency.
