S. Stolz

Computational Mechanics · 2001–2004

A researcher in the Science corpus: 5 papers; trusts 3 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

Whom S. Stolz trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Nikolaus A. Adams
  2. L. Kleiser
  3. G. Em Karniadakis

Who trusts S. Stolz overall

  1. Christophe Bogey
  2. Sergio Pirozzoli
  3. Nikolaus A. Adams
  4. M. Loginov
  5. Christophe Bailly
  6. Philipp Schlatter
  7. Matteo Bernardini
  8. M. Chevalier
  9. Vito Pasquariello
  10. Alfredo Soldati

Papers from S. Stolz's perspective

Ordered by how strongly the researchers S. Stolz trusts endorse each paper. Same corpus, a different order for every researcher.

  1. A Spectral Vanishing Viscosity Method for Large-Eddy Simulations
  2. Physics-informed neural networks for inverse problems in nano-optics and metamaterials.
  3. Adversarial Uncertainty Quantification in Physics-Informed Neural Networks
  4. Physics-informed neural networks for high-speed flows
  5. Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks (SIAM Journal on Scientific Computing)
  6. Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems
  7. Adaptive activation functions accelerate convergence in deep and physics-informed neural networks (Journal of Computational Physics)
  8. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
  9. Understanding the difficulty of training deep feedforward neural networks
  10. A Limited Memory Algorithm for Bound Constrained Optimization

Their papers