1170 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 introduces physics-augmented finite element model updating (paFEMU), a transfer learning framework that leverages sparse regression and multi-modal data to rapidly discover interpretable constitutive models while seamlessly integrating them into existing finite element workflows.
This paper advocates for complementing black-box simulation and design tools in nanophotonics with a deeper understanding of the complex roles played by space, time, and additional dimensions.
This perspective argues that differentiable hybrid force fields, which combine physically motivated functional forms with neural network corrections, resolve the critical trade-off between speed, accuracy, and calibratability to enable scalable, closed-loop autonomous discovery of electrolytes.
The paper introduces Tensor-Augmented Convolutional Neural Networks (TACNN), a physically-guided shallow architecture that replaces conventional kernels with generic tensors to capture high-order feature correlations, achieving competitive accuracy on Fashion-MNIST with significantly fewer layers than deep CNNs like VGG-16 and GoogLeNet.
This paper proposes a Q-learning model that couples exploration rates with local reputation differences and employs asymmetric, state-dependent reputation updates, demonstrating that this joint mechanism significantly promotes the evolution of cooperation by incentivizing high-reputation agents to exploit known strategies while motivating low-reputation agents to explore new cooperative behaviors.
This paper introduces a direction-aware topological data analysis framework that embeds the compression axis into filtration functions to predict Young's modulus in porous materials, demonstrating superior accuracy over traditional direction-agnostic descriptors—particularly for anisotropic structures—while achieving performance comparable to convolutional neural networks with a more compact and transferable representation.
The paper introduces SMC-AI, a scalable algorithmic framework that leverages AI accelerators to achieve the largest reported ML-accelerated atomistic simulation of 4 trillion atoms while decoupling machine learning models from the simulation process to facilitate future integration and portability.
This paper introduces two hard-constrained Physics-informed Neural Network (PINN) formulations—the windowing and buffer approaches—that embed interface physics directly into the solution representation to overcome the accuracy limitations of soft-constraint methods, with the buffer approach demonstrating superior robustness for complex two-dimensional interface problems.
This paper introduces "Integral Decimation," a quantum-inspired algorithm that decomposes multidimensional integrals into a spectral tensor train representation to overcome the curse of dimensionality, enabling efficient and accurate calculations of free energy, entropy, and quantum dynamics in high-dimensional systems where conventional methods fail.
This paper demonstrates that athermal disordered sphere packings subjected to cyclic inverse design evolve toward a marginally absorbing manifold that encodes memory of the training range through return-point memory, driven by gradient discontinuities in the trained quantities.