activity
20242026
most citedCalibration and Validation of a Phase-Field Model of Brittle Fracture within the Damage Mechanics Challenge

10 citations · 13 across the 4 of their papers we have counts for

collaborators

5 papers

cs.AR2026

GarageWorks: A Shared Testbench Abstraction for Software Simulation and FPGA Testing

Kazutomo Yoshii

GarageWorks is a lightweight FPGA testing framework that enables the same cocotb testbench to be used for both software RTL simulation and FPGA-accelerated hardware simulation. A s…

physics.comp-ph2025

Predicting Crack Nucleation and Propagation in Brittle Materials Using Deep Operator Networks with Diverse Trunk Architectures

Elham Kiyani, Manav Manav, Nikhil Kadivar +2

Phase-field modeling reformulates fracture problems as energy minimization problems and enables a comprehensive characterization of the fracture process, including crack nucleation…

physics.app-ph2024

MeltpoolINR: Predicting temperature field, melt pool geometry, and their rate of change in laser powder bed fusion

Manav Manav, Nathanael Perraudin, Yunong Lin +4

We present a data-driven, differentiable neural network model designed to learn the temperature field, its gradient, and the cooling rate, while implicitly representing the melt po…

cs.CE2024★ 10 cited

Calibration and Validation of a Phase-Field Model of Brittle Fracture within the Damage Mechanics Challenge

Jonas Heinzmann, Pietro Carrara, Chenyi Luo +6

In the context of the Damage Mechanics Challenge, we adopt a phase-field model of brittle fracture to blindly predict the behavior up to failure of a notched three-point-bending sp…

physics.app-ph2024★ 3 cited

Phase-Field Modeling of Fracture with Physics-Informed Deep Learning

M. Manav, R. Molinaro, S. Mishra +1

We explore the potential of the deep Ritz method to learn complex fracture processes such as quasistatic crack nucleation, propagation, kinking, branching, and coalescence within t…