Date/Time : 27 Jul 2023 15:00
Venue : S17-04-06 (SR 1)
Speaker : Weinan E, AISI and Peking University

For many years, the lack of good algorithms has severely limited our ability to conduct scientific research. At its heart, the difficulty comes from the notorious “curse of dimensionality” problem. Deep learning is exactly the kind of tool needed to address this problem. In the last few years, we have seen a tremendous amount of scientific progress made as a result of the AI revolution, both in our ability to make use of the fundamental principles of physics and in our ability to make use of experimental data.

In this talk, I will analyze the origin of the AI for Science revolution, review some of the major progresses made so far, and discuss how it will impact the way we do scientific research.

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Date/Time : 27 Jul 2023 16:30
Venue : S17 #04-05 (SR2)
Speaker : Li Yingzhou, Fudan University

We propose a quantum-classical hybrid variational algorithm, the quantum orbital minimization method (qOMM), for obtaining the ground state and low-lying excited states of a Hermitian operator. Given parametrized ansatz circuits representing eigenstates, qOMM implements quantum circuits to represent the objective function in the orbital minimization method and adopts a classical optimizer to minimize the objective function with respect to the parameters in ansatz circuits. The objective function has an orthogonality constraint implicitly embedded, which allows qOMM to apply a different ansatz circuit to each input reference state. We carry out numerical simulations that seek to find excited states of H2, LiH, and a toy model consisting of four hydrogen atoms arranged in a square lattice in the STO3G basis with UCCSD ansatz circuits. Comparing the numerical results with existing excited states methods, qOMM is less prone to getting stuck in local minima and can achieve convergence with more shallow ansatz circuits.

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Date/Time : 30 Jun 2023 22:00
Venue : via Zoom
Speaker : Dawei Shen, Sorbonne University

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https://nus-sg.zoom.us/j/83328843795?pwd=bUEvRU5UaVJad054UFUzV2RaUTZsQT09

Meeting ID: 833 2884 3795
Passcode: 706950

I will start with an introduction of the Einstein-vacuum equations and the history of stability problems of black holes. Then, I will talk about the double null foliations of spacetimes, which induce the null structure equations and Bianchi equations. Next, I will introduce the linearization procedure and the linearized equations. Finally, I will present my main result, which provides a new proof of Kerr stability in external regions, which was first obtained by Caciotta-Nicolò in 2010 using vectorfield method.

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Date/Time : 28 Jun 2023 16:00
Venue : S17 #04-06 (SR1)
Speaker : Dai Shuyang, Wuhan University

In this talk, we present some modeling and simulations of heterogeneous structures in material sciences. The first part is the multiscale continuum model of defect structures in crystalline materials. The interface structure for twist, tilt and misfit grain boundaries are described by the dislocation network. The model incorporates both the anisotropy elasticity of each grain in crystalline materials and the molecular dynamics calculation informed interaction between two bulks, i.e., the nonlinear generalized stacking-fault energy. The second part is the simulations of dielectric inhomogeneous systems. We proposed several approaches to accelerate the calculation of static and dynamic properties of charged polymers in 3D dielectric inhomogeneity system.

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Date/Time : 16 Jun 2023 15:00
Venue : S17 #04-04 (SR3)
Speaker : Zhu Feng, University of Wisconsin

Putting a hyperbolic metric on a complete finite-type surface gives us a linear representation (the holonomy representation) with many nice geometric and dynamical properties: for instance it is discrete and faithful, and in fact stably quasi-isometrically embedded. This picture can be generalised in (at least) two ways. First, the notion of geometric finiteness generalises this picture in the context of rank-one Lie groups such as PSL(2,R) or PSL(2,C). Second, Anosov representations generalise this picture to higher-rank Lie groups such as PSL(d,K) for d>2.

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Date/Time : 17 May 2023 14:00
Venue : S17 #04-06 (SR1)
Speaker : Susanne Brenner, Lousiana State University

Elliptic optimal control problems are infinite dimensional least-squares data fitting problems where the models are defined by second order elliptic boundary value problems. In the presence of pointwise constraints on the state variable, the traditional approach becomes more challenging due to the reduced regularity of the adjoint state. In this talk I will explain how to adopt finite element methods originally designed for fourth order elliptic boundary value problems to this class of optimal control problems and present a unified framework for their analysis.

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Date/Time : 17 May 2023 10:30
Venue : Block A #10-01 (Energy Studies Institute)
Speaker : Gazi Salah Uddin, Linköping University

Solar adoption exploded until 2015 in Bangladesh, setting a precedent for electrifying previously unconnected areas worldwide. This study tries to measure the welfare effects of solar adoption using the three rounds of the Bangladesh Integrated Household Surveys. We applied ordinary least squares and propensity score matching techniques to estimate the welfare effects of solar adoption. We found that solar adoption is associated with higher income, expenditure, asset value growth, and a massive reduction in kerosene expenditure than non-adopters. Other findings include solar households that leave sharecropping and pursue trading and poultry farming and children who benefit from solar adoption regarding education and nutrition.

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Date/Time : 15 May 2023 16:00
Venue : S17 #04-06 (SR1)
Speaker : Jeong Han Kim, Korea Institute for Advanced Study

Majority dynamics on a graph G is a deterministic process such that every vertex updates its +/- 1-assignment according to the majority assignment on its neighbor simultaneously at each step. Benjamini, Chan, O’Donnell, Tamuz and Tan conjectured that, in the Erdos-Renyi random graph G(n,p), the random initial +/- 1-assignment converges to a 99%-agreement with high probability whenever p = \omega(1/n). This conjecture was first confirmed for p > \lambda n^{-1/2} for a large constant \lambda by Fountoulakis, Kang and Makai. Although this result has been reproved recently by Tran and Vu and by Berkowitz and Devlin, it was unknown whether the conjecture holds for p < \lambda n^{-1/2} . We break this \Omega(n^{-1/2})-barrier by proving the conjecture for sparser random graphs G(n,p), where \lambda’ n^{-3/5} \log n < p < \lambda n^{-1/2} with a large constant \lambda’ > 0.

(Joint work with D. Chakraborti, J. Lee, and T. Tran)

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Date/Time : 12 May 2023 14:00
Venue : S17 #04-06 (SR1)
Speaker : Chan Hon Fu Raymond, City University of Hong Kong

With the advances in sensor technology, data has become ubiquitous. To make sense of data we have to solve higher and higher dimensional problems that may seem intractable. However, many high-dimensional problems have solutions that live in low-dimensional spaces. Sparsity is a way to exploit the low-dimensional structure of the solutions to obtain feasible solutions for high-dimensional problems. In this talk, we will see how sparsity can be applied to facilitate the solutions of imaging problems, including magnetic resonance imaging, single-molecule localisation microscopy, and ground-based astronomy.

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