activity
20122026
most citedMultiwavelet-based Operator Learning for Differential Equations

67 citations · 101 across the 62 of their papers we have counts for

collaborators
Showing 2021Show all

7 papers · 1 filter

cs.AI2021

Trust-aware Control for Intelligent Transportation Systems

Mingxi Cheng, Junyao Zhang, Shahin Nazarian +2

Many intelligent transportation systems are multi-agent systems, i.e., both the traffic participants and the subsystems within the transportation infrastructure can be modeled as i…

cs.LG2021★ 67 cited

Multiwavelet-based Operator Learning for Differential Equations

Gaurav Gupta, Xiongye Xiao, Paul Bogdan

The solution of a partial differential equation can be obtained by computing the inverse operator map between the input and the solution space. Towards this end, we introduce a \te…

cs.LG2021

Non-Markovian Reinforcement Learning using Fractional Dynamics

Gaurav Gupta, Chenzhong Yin, Jyotirmoy V. Deshmukh +1

Reinforcement learning (RL) is a technique to learn the control policy for an agent that interacts with a stochastic environment. In any given state, the agent takes some action, a…

eess.SY2021★ 1 cited

Minimum Structural Sensor Placement for Switched Linear Time-Invariant Systems and Unknown Inputs

Emily A. Reed, Guilherme Ramos, Paul Bogdan +1

In this paper, we study the structural state and input observability of continuous-time switched linear time-invariant systems and unknown inputs. First, we provide necessary and s…

math.OC2021★ 1 cited

A scalable distributed dynamical systems approach to compute the strongly connected components and diameter of networks

Emily A. Reed, Guilherme Ramos, Paul Bogdan +1

Finding strongly connected components (SCCs) and the diameter of a directed network play a key role in a variety of discrete optimization problems, and subsequently, machine learni…

cs.LG2021★ 2 cited

Learning Hyperbolic Representations of Topological Features

Panagiotis Kyriakis, Iordanis Fostiropoulos, Paul Bogdan

Learning task-specific representations of persistence diagrams is an important problem in topological data analysis and machine learning. However, current state of the art methods…