计算物理
Physical systems with complex unsteady dynamics, such as fluid flows, are often poorly represented by a single mean solution. For many practical applications, it is crucial to access the full distribution of possible states, from which…
Discrete unified gas-kinetic scheme (DUGKS) is a multi-scale numerical method for flows from continuum limit to free molecular limit, and is especially suitable for the simulation of multi-scale flows, benefiting from its multi-scale…
Natural systems often exhibit chaotic behavior in their space-time evolution. Systems transiting between chaos and order manifest a potential to compute, as shown with cellular automata and artificial neural networks. We demonstrate that…
Numerical simulation of strong-field quantum electrodynamics (SFQED) processes is an essential step towards current and future high-intensity laser experiments. The complexity of SFQED phenomena and their stochastic nature make them…
We present a real-space method for computing the random phase approximation (RPA) correlation energy within Kohn-Sham density functional theory, leveraging the low-rank nature of the frequency-dependent density response operator. In…
Interpreting scattered acoustic and electromagnetic wave patterns is a computational task that enables remote imaging in a number of important applications, including medical imaging, geophysical exploration, sonar and radar detection, and…
Stochastic Subspace Identification (SSI) is widely used in modal analysis of engineering structures, known for its numerical stability and high accuracy in modal parameter identification. SSI methods are generally classified into two types:…
This paper introduces JAX-BTE, a GPU-accelerated, differentiable solver for the phonon Boltzmann Transport Equation (BTE) based on differentiable programming. JAX-BTE enables accurate, efficient and differentiable multiscale thermal…
This paper extends previous work (Groom et al., \emph{Artif. Intell. Earth Syst.}, 2024) in applying the entropy-optimal Sparse Probabilistic Approximation (eSPA) algorithm to predict ENSO phase, defined by thresholding the Ni\~no3.4 index.…
Direct minimization method on the complex Stiefel manifold in Kohn-Sham density functional theory is formulated to treat both finite and extended systems in a unified manner. This formulation is well-suited for scenarios where…
Cryo-computing - both classical and quantum, is severely limited by the absence of a suitable cryo-memory. The challenge both in terms of energy efficiency and speed have been known for decades, but so far conventional technologies have not…
Molecular dynamics (MD) simulations play a crucial role in scientific research. Yet their computational cost often limits the timescales and system sizes that can be explored. Most data-driven efforts have been focused on reducing the…
Machine learning is increasingly transforming various scientific fields, enabled by advancements in computational power and access to large data sets from experiments and simulations. As artificial intelligence (AI) continues to grow in…
Mass concrete plays a crucial role in large-scale projects such as water conservancy hubs and transportation infrastructure. Due to its substantial volume and poor thermal conductivity, the accumulation of hydration heat during the curing…
In this paper, we present a 2D numerical model developed to simulate the dynamics of soft, deformable particles. To accommodate significant particle deformations, the particle surface is represented as a narrow shell composed of mass points…
We introduce the Velocity Jumps approach, denoted as JUMP, a new class of Molecular dynamics integrators, replacing the Langevin dynamics by a hybrid model combining a classical Langevin diffusion and a piecewise deterministic Markov…
This paper introduces a method to identify traps in molecular charge transport networks as obtained by multiscale modeling of organic semiconductors. Depending on the materials, traps can be defect-like single molecules or clusters of…
Data-driven and non-intrusive DMDc and DMDspc models successfully expedite the reconstruction and forecasting of CO2 fluid flow with acceptable accuracy margins, aiding in the rapid optimization of geological CO2 storage forecast and…
The high-energy repulsive interaction between nuclei at distances much smaller than the equilibrium bond length is the key quantity determining the nuclear stopping power and atom scattering in keV and MeV radiation events. This interaction…
With recent advancements in machine learning for interatomic potentials, Python has become the go-to programming language for exploring new ideas. While machine-learning potentials are often developed in Python-based frameworks, existing…