Search Results for author:
Found 9 papers, 5 papers with code
SoftTiger: A Clinical Foundation Model for Healthcare Workflows
We introduce SoftTiger, a clinical large language model (CLaM) designed as a foundation model for healthcare workflows.
Accelerated Sampling of Rare Events using a Neural Network Bias Potential
In particular, we approximate the variance-free bias potential function with DNNs which is trained to maximize the probability of rare event transition under the importance potential function.
TigerBot: An Open Multilingual Multitask LLM
We release and introduce the TigerBot family of large language models (LLMs), consisting of base and chat models, sized from 7, 13, 70 and 180 billion parameters.
Prediction of Effective Elastic Moduli of Rocks using Graph Neural Networks
This study presents a Graph Neural Networks (GNNs)-based approach for predicting the effective elastic moduli of rocks from their digital CT-scan images.
Evaluating the Transferability of Machine-Learned Force Fields for Material Property Modeling
Machine-learned force fields have generated significant interest in recent years as a tool for molecular dynamics (MD) simulations, with the aim of developing accurate and efficient models that can replace classical interatomic potentials.
Multi-scale Deep Neural Network (MscaleDNN) for Solving Poisson-Boltzmann Equation in Complex Domains
In this paper, we propose multi-scale deep neural networks (MscaleDNNs) using the idea of radial scaling in frequency domain and activation functions with compact support.
Multi-scale Deep Neural Networks for Solving High Dimensional PDEs
In this paper, we propose the idea of radial scaling in frequency domain and activation functions with compact support to produce a multi-scale DNN (MscaleDNN), which will have the multi-scale capability in approximating high frequency and high dimensional functions and speeding up the solution of high dimensional PDEs.
A Phase Shift Deep Neural Network for High Frequency Approximation and Wave Problems
In this paper, we propose a phase shift deep neural network (PhaseDNN), which provides a uniform wideband convergence in approximating high frequency functions and solutions of wave equations.
PhaseDNN - A Parallel Phase Shift Deep Neural Network for Adaptive Wideband Learning
Due to the phase shift, each DNN achieves the speed of convergence as in the low frequency range.
