1164 papers
Computational physics bridges the gap between abstract theory and real-world observation by using powerful computers to solve complex physical problems. This field allows scientists to simulate everything from the collision of subatomic particles to the swirling dynamics of galaxies, offering insights that traditional experiments alone cannot provide.
On Gist.Science, we continuously process every new preprint in this category from arXiv to make these breakthroughs accessible to everyone. Each entry is accompanied by both a clear, plain-language explanation and a detailed technical summary, ensuring that researchers and curious readers alike can grasp the significance of the latest findings without getting lost in dense equations.
Below are the latest papers in computational physics, curated to keep you at the forefront of this rapidly evolving discipline.
This paper evaluates the hydrodynamic behavior and validity limits of an immersed boundary–lattice Boltzmann method for wetting problems by comparing its contact-line model and thin-film formation against boundary element and volume of fluid solvers.
This paper proposes an approximate Hamiltonian simulation algorithm that significantly reduces circuit depth and two-qubit gate counts for quantum fluid simulations by optimizing operator truncation, thereby preserving macroscopic flow characteristics and balancing theoretical errors with hardware noise to enable feasible simulations on near-term quantum devices.
This study combines computational modeling and astrochemical simulations to reveal that while amorphous solid water ice shields interstellar thiocyanates HCSCN and HCSCCH from thermal desorption through strong binding, it simultaneously creates a "survival paradox" where these deeply trapped molecules become more vulnerable to photodissociation due to enhanced UV absorption cross-sections.
This paper demonstrates through ground-state quantum Monte Carlo calculations that the energy of a mobile impurity in a lattice Bose gas exhibits scale invariance at the Mott-superfluid critical point, establishing impurity spectroscopy as a viable method for probing the critical properties of quantum phase transitions.
This paper introduces two deep learning autoregressive surrogate models—a Koopman-based Transformer and a ConvLSTM-UNet—that accurately and efficiently predict the temporal evolution of 2D ideal magnetohydrodynamic Kelvin-Helmholtz instabilities while preserving key physical structures and invariants at a substantially reduced computational cost compared to direct numerical simulations.
This paper theoretically clarifies the stability mechanisms of the momentum parameter in the SPRING algorithm for Variational Monte Carlo optimization and proposes PRIME-SR, a tuning-free adaptive method that achieves comparable performance to optimally tuned SPRING while significantly improving robustness.
This paper resolves the challenge of controlling energy dissipation in non-spherical particle contact by deriving a structure-preserving damping formulation from first principles that ensures consistent contact-point restitution through a unique damping law aligned with the system's intrinsic translational-rotational coupling.
This paper presents a physics-informed neural network framework that integrates variational learning with Magnus expansion to optimize control protocols and maximize Quantum Fisher Information in time-dependent many-body systems, demonstrating superior performance over reference solutions for up to six qubits.
This paper proposes and rigorously analyzes efficient, unconditionally stable, and second-order accurate Alternating Direction Implicit (ADI) schemes, enhanced with a kernel-free boundary integral method and level set technique, to solve three-dimensional heat equations and reaction-diffusion problems featuring irregular boundaries, interfaces, and free boundaries such as those in dendritic solidification.
SeQuant is an open-source library that employs a novel graph-theoretic tensor network canonicalizer to unify symbolic and numerical tensor algebra, enabling efficient handling of complex expressions involving symmetries, non-commutative operators, and parametric mode dependencies for applications in quantum simulation and data science.