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Found 14 papers, 1 papers with code
Dynamic PDB: A New Dataset and a SE(3) Model Extension by Integrating Dynamic Behaviors and Physical Properties in Protein Structures
Despite significant progress in static protein structure collection and prediction, the dynamic behavior of proteins, one of their most vital characteristics, has been largely overlooked in prior research.
Fast Iterative Solver For Neural Network Method: II. 1D Diffusion-Reaction Problems And Data Fitting
We present a factorization of the mass matrix that enables solving the systems of linear equations in ${\cal O}(n)$ operations.
Weak Generative Sampler to Efficiently Sample Invariant Distribution of Stochastic Differential Equation
Sampling invariant distributions from an Ito diffusion process presents a significant challenge in stochastic simulation.
A Structure-Guided Gauss-Newton Method for Shallow ReLU Neural Network
In this paper, we propose a structure-guided Gauss-Newton (SgGN) method for solving least squares problems using a shallow ReLU neural network.
Qubit-Wise Architecture Search Method for Variational Quantum Circuits
Considering the noise level limit, one crucial aspect for quantum machine learning is to design a high-performing variational quantum circuit architecture with small number of quantum gates.
Residual-Quantile Adjustment for Adaptive Training of Physics-informed Neural Network
Thus, we propose the Residual-Quantile Adjustment (RQA) method for a better weight choice for each training sample.
Learn Quasi-stationary Distributions of Finite State Markov Chain
We propose a reinforcement learning (RL) approach to compute the expression of quasi-stationary distribution.
Least-Squares Neural Network (LSNN) Method For Scalar Nonlinear Hyperbolic Conservation Laws: Discrete Divergence Operator
A least-squares neural network (LSNN) method was introduced for solving scalar linear and nonlinear hyperbolic conservation laws (HCLs) in [7, 6].
RitzNet: A Deep Neural Network Method for Linear Stress Problems
This paper presents RitzNet, an unsupervised learning method which takes any point in the computation domain as input, and learns a neural network model to output its corresponding function value satisfying the underlying governing PDEs.
Self-adaptive deep neural network: Numerical approximation to functions and PDEs
Designing an optimal deep neural network for a given task is important and challenging in many machine learning applications.
Least-Squares ReLU Neural Network (LSNN) Method For Linear Advection-Reaction Equation
This paper studies least-squares ReLU neural network method for solving the linear advection-reaction problem with discontinuous solution.
Least-Squares ReLU Neural Network (LSNN) Method For Scalar Nonlinear Hyperbolic Conservation Law
We introduced the least-squares ReLU neural network (LSNN) method for solving the linear advection-reaction problem with discontinuous solution and showed that the method outperforms mesh-based numerical methods in terms of the number of degrees of freedom.
Deep least-squares methods: an unsupervised learning-based numerical method for solving elliptic PDEs
This paper studies an unsupervised deep learning-based numerical approach for solving partial differential equations (PDEs).
An Empirical Study on Academic Commentary and Its Implications on Reading and Writing
The relationship between reading and writing (RRW) is one of the major themes in learning science.
