Somdatta Goswami
Statistical and Nonlinear Physics · 2016–2021
A researcher in the Science corpus: 6 papers; trusts 26 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Statistical and Nonlinear Physics — 55 citations into it; #6,406 of 10,127 by trust here, #2,631 by citations
- Mechanics of Materials — 50 citations into it; #6,151 of 6,291 by trust here, #2,341 by citations
- Computational Mechanics — 48 citations into it
- Artificial Intelligence — 35 citations into it
- Statistics and Probability — 23 citations into it
- AI for Physical Sciences — 14 citations into it
- Computational Theory and Mathematics — 14 citations into it
- Computer Vision and Pattern Recognition — 8 citations into it
Whom Somdatta Goswami trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Maizar Raissi
- C. Miehé
- Xavier Glorot
- G. Em Karniadakis
- Michael J. Borden
- Diederik P. Kingma
- Blaise Bourdin
- Paris Perdikaris
- Yoshua Bengio
- F. Welschinger
Who trusts Somdatta Goswami overall
- Chengping Rao
- Pengzhan Jin
- Minglang Yin
- Xu Li
- S. Niaki
- Yue Yu
- G. Em Karniadakis
- Yang Liu
- Shuai Meng
- Lu Lu
Papers from Somdatta Goswami's perspective
Ordered by how strongly the researchers Somdatta Goswami trusts endorse each paper. Same corpus, a different order for every researcher.
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Going deeper with convolutions
- ImageNet: A large-scale hierarchical image database
Their papers
- Reliability analysis of structures by iterative improved response surface method (2016) — 187 citations
- A physics-informed variational DeepONet for predicting the crack path in brittle materials (2021) — 310 citations
- A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data (2021) — 711 citations
- Adaptive fourth-order phase field analysis using deep energy minimization (2020)
- An Energy Approach to the Solution of Partial Differential Equations in Computational Mechanics via Machine Learning: Concepts, Implementation and Applications (2019) — 1,881 citations
- Transfer learning enhanced physics informed neural network for phase-field modeling of fracture (2019) — 829 citations