121 papers
This paper presents a computational framework that combines database analysis with microscopic modeling to predict and identify promising new molecular single-photon emitters, successfully benchmarking the approach on dibenzoterrylene and discovering novel candidates like a chiral emitter.
This paper presents a robust and unconditionally stable space-time Galerkin time-domain boundary element method for predicting acoustic scattering and shielding by complex geometries, which is validated through analytical cases and successfully applied to a practical trailing edge-mounted propeller scenario with good agreement to experimental data.
This paper compares cache-conscious sort-and-sweep and tree-code algorithms for neighborhood computation in two-dimensional discrete element method simulations, finding that while tree codes offer slightly better performance and improved parallelization potential, they come at the cost of significantly increased code complexity.
This paper empirically demonstrates that while Kolmogorov-Arnold Networks (KANs) can compete with MLPs on simple univariate residuals in hard-constrained recurrent physics-informed architectures, they suffer from severe hyperparameter fragility, instability in deeper configurations, and consistent failure on multiplicative terms, ultimately revealing limitations in their additive inductive bias for modeling state coupling in oscillatory systems.
This paper introduces Topology-Aware PINNs (TAPINN), a novel framework that employs supervised metric regularization and alternating optimization to effectively resolve spectral bias and mode collapse in multi-regime physics-informed neural networks, achieving superior convergence stability and accuracy compared to standard and hypernetwork-based baselines.
This paper introduces a gradient-based optimization method using straight-through Gumbel-Softmax estimation to enable efficient parameter inference and inverse design in exact stochastic kinetic models by approximating gradients through continuous relaxation while preserving discrete stochastic dynamics in the forward pass.
This paper presents an energy-conserving contact dynamics framework for arbitrary convex nonspherical rigid-body particles in 2D and 3D that prevents overlap while enabling continuous force evaluation, thereby providing a robust foundation for modeling the structural and transport properties of complex colloidal and granular systems.
This paper introduces B-ODIL, a Bayesian extension of the Optimization of a Discrete Loss (ODIL) method that integrates PDE-based prior knowledge with data likelihood to solve inverse problems with quantified uncertainties, demonstrating its effectiveness through synthetic benchmarks and a clinical application for estimating brain tumor concentration from MRI scans.
This paper presents a quadrature-based model reduction framework for Lagrangian hydrodynamics that utilizes a strongly energy-conservative variant of the empirical quadrature procedure to achieve near machine-precision total energy conservation while maintaining accuracy comparable to standard methods.
This study introduces a factorized-implicit Fourier Neural Operator (F-IFNO) framework that enhances long-term stability and accuracy in predicting three-dimensional turbulence by integrating uncertainty quantification and error propagation analysis to overcome the limitations of traditional models and existing neural operators.
This paper presents an exact frequency-domain formulation of pendulum-like systems that unifies oscillatory, separatrix, and rotational regimes under a single universal spectral kernel, revealing that regime transitions are symmetry-driven reorganizations in frequency space rather than changes in the underlying spectral structure.
This study presents the first systematic comparison of seven hydrodynamic codes simulating the unstratified streaming instability, revealing broad qualitative agreement across methods while identifying dust modeling choices and resolution as key factors influencing quantitative density statistics and highlighting the superior energy efficiency and scalability of GPU-based implementations.
This paper introduces "Ara," an LLM-based agentic workflow that leverages chemical priors to efficiently navigate the design space of covalent organic frameworks, successfully identifying durable and active photocatalysts for solar hydrogen production with significantly higher hit rates and faster convergence than random search or Bayesian optimization.
This study presents an inverse-design framework combining genetic algorithms with frequency-domain micromagnetics to successfully discover unconventional two-dimensional magnonic crystal structures featuring large band gaps, thereby addressing the challenges of optimizing complex lattice geometries for targeted spin-wave properties.
This study employs advanced GAP interatomic potentials, molecular dynamics, and solid-state nudged elastic band calculations to elucidate the atomic-scale mechanisms and pressure-dependent nucleation barriers driving the phase transformations of silicon, particularly linking simulation results to experimental observations of hexagonal phase formation from BC8/R8 precursors.
By combining the energy resolution limits of quantum sensors with the human brain's metabolic power, this paper establishes a technology-independent fundamental bound of approximately 2.2 Mbit/s on the information capacity of magnetoencephalography, revealing an inherent spatio-temporal trade-off that limits the spatial complexity of detectable neural patterns.
This paper introduces measurement-dressed imaginary-time evolution (MDITE) as a novel framework for studying mixed-state phase transitions driven by the competition between coherence-restoring dynamics and decoherence, demonstrating the existence of new critical behaviors in one- and two-dimensional models that fall outside known universality classes.
This paper extends first-principles spin-lattice relaxation theory to include three-phonon processes, demonstrating that while these contributions are negligible for the studied Chromium nitride complex under experimental conditions—thereby validating the weak coupling assumption—the framework reveals that slightly stronger coupling could make three-phonon effects significant at room temperature.
This paper introduces a Fourier Neural Operator-based surrogate model that rapidly and accurately predicts angle- and energy-resolved proton transport and neutron production in proton therapy, achieving Monte Carlo-level accuracy within seconds to enable real-time adaptive range verification and neutron dose estimation.
This paper proposes a simple numerical method based on inverse transform sampling to efficiently load relativistic Maxwellian-type distributions by approximating the cumulative distribution of a shifted-Maxwellian energy distribution with an invertible function to generate random energy and momentum variates.