arXiv subjects

Lixin Lu

Publications and source records attributed to Lixin Lu.

5 recordsLinked to original sources

arXiv2026

A Sequence-to-Sequence ConvLSTM Approach for Leaf Area Index Forecasting over the South-Central United States

Leaf Area Index (LAI) is a fundamental biophysical variable governing land-atmosphere interactions; however, LAI forecasting at high spatial resolution remains an unsolved challenge. While recent machine learning approaches have demonstrated LAI estimation at point or regional scales, none provides a gridded, meteorology-driven prognostic forecast suitable for subseasonal land surface and climate modeling applications. Here we present a sequence-to-sequence Convolutional LSTM (ConvLSTM) framework that generates daily 1-km LAI forecasts up to 30 days ahead, driven by historical LAI sequences and daily meteorological forcing including temperature and precipitation. Trained and evaluated over the South-Central United States -- a region of strong climate gradients and diverse vegetation -- the model achieves a domain-averaged RMSE of 0.36 at a 30-day lead time, more than a third lower than the persistence baseline. Forecast skill remains robust across seasons, geographic distributions, and plant functional types, including forests, grasslands, shrublands, and croplands. To our knowledge, this is the first demonstration of skillful LAI forecasting at a 30-day horizon at 1-km resolution.

physics.ao-ph↗

arXiv2026

Non-Negative Least Squares Reweighting and Pruning of Quadrature Grids for Tensor Hypercontraction

Tensor hypercontraction provides an attractive four-center two-electron repulsion integral format that can lower the scaling of many electronic structure methods while only requiring O(N^2) memory. However, in its grid-based least-squares incarnation, tensor hypercontraction requires the tedious design of compact spatial quadrature grids to achieve efficiency and accuracy, representing a bottleneck for widespread application. To simplify grid generation, we devise a reweighting scheme in which the grid weights are optimized to ensure accurate reproduction of the atomic orbital overlap matrix by numerical integration. By casting this fitting task as a non-negative least-squares problem, we obtain a black-box methodology that not only yields robust grids for tensor hypercontraction as well as numerical integration of other integrals but also prunes the grids by zeroing quadrature weights for insignificant points.

physics.chem-ph↗

arXiv2025

Predicting Tropical Cyclone Track Forecast Errors using a Probabilistic Neural Network

A new method for estimating tropical cyclone track uncertainty is presented and tested. This method uses a neural network to predict a bivariate normal distribution, which serves as an estimate for track uncertainty. We train the network and make predictions on forecasts from the National Hurricane Center (NHC), which currently uses static error distributions based on forecasts from the past five years for most applications. The neural network-based method produces uncertainty estimates that are dynamic and probabilistic. Further, the neural network-based method allows for probabilistic statements about tropical cyclone trajectories, including landfall probability, which we highlight. We show that our predictions are well calibrated using multiple metrics, that our method produces better uncertainty estimates than current NHC approaches, and that our method achieves similar performance to the Global Ensemble Forecast System. Once trained, the computational cost of predictions using this method is negligible, making it a strong candidate to improve the NHC's operational estimations of tropical cyclone track uncertainty.

physics.ao-ph↗

arXiv2024

Prediction of Photodynamics of 200 nm Excited Cyclobutanone with Linear Response Electronic Structure and Ab Initio Multiple Spawning

Simulations of photochemical reaction dynamics have been a challenge to the theoretical chemistry community for some time. In an effort to determine the predictive character of current approaches, we predict the results of an upcoming ultrafast diffraction experiment on the photodynamics of cyclobutanone after excitation to the lowest lying Rydberg state (S$_2$). A picosecond of nonadiabatic dynamics is described with ab initio multiple spawning. We use both time dependent density functional theory and equation-of-motion coupled cluster for the underlying electronic structure theory. We find that the lifetime of the S$_2$ state is more than a picosecond (with both TDDFT and EOM-CCSD). The predicted UED spectrum exhibits numerous structural features, but weak time dependence over the course of the simulations.

physics.chem-ph↗

arXiv2021

Defect-Induced Magnetic Skyrmion in Two-Dimensional Chromium Tri-Iodide Monolayer

Chromium iodide monolayers, which have different magnetic properties in comparison to the bulk chromium iodide, have been shown to form skyrmionic states in applied electromagnetic fields or in Janus-layer devices. In this work, we demonstrate that spin-canted solutions can be induced into monolayer chromium iodide by select substitution of iodide atoms with isovalent impurities. Several concentrations and spatial configurations of halide substitutional defects are selected to probe the coupling between the local defect-induced geometric distortions and orientation of chromium magnetic moments. This work provides atomic-level insight into how atomically precise strain-engineering can be used to create and control complex magnetic patterns in chromium iodide layers and lays out the foundation for investigating the field- and geometric-dependent magnetic properties in similar two-dimensional materials.

physics.comp-ph↗