2 citations · 2 across the 6 of their papers we have counts for
7 papers
LUCAS-MEGA: A Large-Scale Multimodal Dataset for Representation Learning in Soil-Environment Systems
Kuangdai Leng, Simon Jeffery, Panos Panagos +1
Understanding soil is fundamental to agriculture, carbon cycling, and environmental sustainability, yet progress is limited by fragmented and heterogeneous datasets that constrain…
Discovery and recovery of crystalline materials with property-conditioned transformers
Cyprien Bone, Matthew Walker, Bradley A. A. Martin +6
Generative models have recently shown great promise for accelerating the design and discovery of new functional materials. Conditional generation enhances this capacity by allowing…
Deep Learning Evidence for Global Optimality of Gerver's Sofa
Kuangdai Leng, Jia Bi, Jaehoon Cha +2
The Moving Sofa Problem, formally proposed by Leo Moser in 1966, seeks to determine the largest area of a two-dimensional shape that can navigate through an -shaped corridor wit…
GlobalTomo: A global dataset for physics-ML seismic wavefield modeling and FWI
Shiqian Li, Zhi Li, Zhancun Mu +6
Global seismic tomography, taking advantage of seismic waves from natural earthquakes, provides essential insights into the earth's internal dynamics. Advanced Full-waveform Invers…
Zero Coordinate Shift: Whetted Automatic Differentiation for Physics-informed Operator Learning
Kuangdai Leng, Mallikarjun Shankar, Jeyan Thiyagalingam
Automatic differentiation (AD) is a critical step in physics-informed machine learning, required for computing the high-order derivatives of network output w.r.t. coordinates of co…
Padding-free Convolution based on Preservation of Differential Characteristics of Kernels
Kuangdai Leng, Jeyan Thiyagalingam
Convolution is a fundamental operation in image processing and machine learning. Aimed primarily at maintaining image size, padding is a key ingredient of convolution, which, howev…
On the Compatibility between Neural Networks and Partial Differential Equations for Physics-informed Learning
Kuangdai Leng, Jeyan Thiyagalingam
We shed light on a pitfall and an opportunity in physics-informed neural networks (PINNs). We prove that a multilayer perceptron (MLP) only with ReLU (Rectified Linear Unit) or ReL…